Overview

Dataset statistics

Number of variables41
Number of observations6367
Missing cells67306
Missing cells (%)25.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 MiB
Average record size in memory1.8 KiB

Variable types

CAT24
BOOL9
NUM8

Warnings

Parcel Number has a high cardinality: 6365 distinct values High cardinality
Address has a high cardinality: 6291 distinct values High cardinality
Postal Code has a high cardinality: 87 distinct values High cardinality
Neighborhood has a high cardinality: 132 distinct values High cardinality
Sold Date has a high cardinality: 843 distinct values High cardinality
Potential Use has a high cardinality: 78 distinct values High cardinality
Acquisition Date has a high cardinality: 83 distinct values High cardinality
Date evaluated has a high cardinality: 244 distinct values High cardinality
Impervious surface present? has a high cardinality: 202 distinct values High cardinality
Location 1 has a high cardinality: 6317 distinct values High cardinality
Acquisition Date is highly correlated with CountyHigh correlation
County is highly correlated with Acquisition DateHigh correlation
Zoned As has 95 (1.5%) missing values Missing
Latitude has 137 (2.2%) missing values Missing
Longitude has 137 (2.2%) missing values Missing
Sold Date has 3612 (56.7%) missing values Missing
Potential Use has 2162 (34.0%) missing values Missing
Quiet Title has 1086 (17.1%) missing values Missing
Environmental Cleanup Needed has 1086 (17.1%) missing values Missing
Brush Removal Needed has 1086 (17.1%) missing values Missing
Trash Removal Needed has 1086 (17.1%) missing values Missing
Demo Needed has 1085 (17.0%) missing values Missing
Rehab Candidate has 1086 (17.1%) missing values Missing
Market Value Year has 231 (3.6%) missing values Missing
Square Footage has 99 (1.6%) missing values Missing
Acquisition Date has 65 (1.0%) missing values Missing
Foreclosure Year has 3641 (57.2%) missing values Missing
Property Condition has 325 (5.1%) missing values Missing
Program has 5908 (92.8%) missing values Missing
Structure Type has 5632 (88.5%) missing values Missing
Structure Square Footage has 5784 (90.8%) missing values Missing
Number of Bedrooms has 5402 (84.8%) missing values Missing
Number of Full Baths has 5730 (90.0%) missing values Missing
Number of Half Baths has 6356 (99.8%) missing values Missing
Date evaluated has 4939 (77.6%) missing values Missing
Impervious surface present? has 5227 (82.1%) missing values Missing
Building repairable, water service will be needed has 5201 (81.7%) missing values Missing
Market Value is highly skewed (γ1 = 44.87347723) Skewed
Square Footage is highly skewed (γ1 = 23.66894387) Skewed
Structure Square Footage is highly skewed (γ1 = 24.14511907) Skewed
Parcel Number is uniformly distributed Uniform
Address is uniformly distributed Uniform
Location 1 is uniformly distributed Uniform

Reproduction

Analysis started2020-12-12 20:30:23.959526
Analysis finished2020-12-12 20:30:33.742445
Duration9.78 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Parcel Number
Categorical

HIGH CARDINALITY
UNIFORM

Distinct6365
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
JA30130221100000000
 
2
JA28820130300000000
 
2
JA29640252900000000
 
1
JA29620150505000000
 
1
JA31420121100000000
 
1
Other values (6360)
6360 
ValueCountFrequency (%) 
JA301302211000000002< 0.1%
 
JA288201303000000002< 0.1%
 
JA296402529000000001< 0.1%
 
JA296201505050000001< 0.1%
 
JA314201211000000001< 0.1%
 
JA297102603000000001< 0.1%
 
JA485100823000000001< 0.1%
 
30-710-31-28-00-0-00-0001< 0.1%
 
47-630-07-03-00-0-00-0001< 0.1%
 
JA288101523000000001< 0.1%
 
JA297301909000000001< 0.1%
 
JA298401227000000001< 0.1%
 
JA284302501000000001< 0.1%
 
JA314202010000000001< 0.1%
 
30-620-23-19-00-0-00-0001< 0.1%
 
JA285300306000000001< 0.1%
 
JA149401337000000001< 0.1%
 
JA297402817000000001< 0.1%
 
JA282301120000000001< 0.1%
 
JA313100204010000001< 0.1%
 
JA288101705000000001< 0.1%
 
JA477101401000000001< 0.1%
 
JA284304224000000001< 0.1%
 
JA284202417000000001< 0.1%
 
JA476400123020000001< 0.1%
 
Other values (6340)634099.6%
 
2020-12-12T15:30:33.819511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6363 ?
Unique (%)99.9%
2020-12-12T15:30:33.895076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length19
Mean length19.48500079
Min length14

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
06299650.8%
 
299418.0%
 
196417.8%
 
371205.7%
 
A57424.6%
 
J57414.6%
 
450174.0%
 
-43473.5%
 
842733.4%
 
926132.1%
 
623041.9%
 
522321.8%
 
720921.7%
 
C1< 0.1%
 
L1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number10822987.2%
 
Uppercase Letter114859.3%
 
Dash Punctuation43473.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A574250.0%
 
J574150.0%
 
C1< 0.1%
 
L1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
06299658.2%
 
299419.2%
 
196418.9%
 
371206.6%
 
450174.6%
 
842733.9%
 
926132.4%
 
623042.1%
 
522322.1%
 
720921.9%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4347100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common11257690.7%
 
Latin114859.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A574250.0%
 
J574150.0%
 
C1< 0.1%
 
L1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
06299656.0%
 
299418.8%
 
196418.6%
 
371206.3%
 
450174.5%
 
-43473.9%
 
842733.8%
 
926132.3%
 
623042.0%
 
522322.0%
 
720921.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII124061100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
06299650.8%
 
299418.0%
 
196417.8%
 
371205.7%
 
A57424.6%
 
J57414.6%
 
450174.0%
 
-43473.5%
 
842733.4%
 
926132.1%
 
623041.9%
 
522321.8%
 
720921.7%
 
C1< 0.1%
 
L1< 0.1%
 

Property Class
Categorical

Distinct15
Distinct (%)0.2%
Missing4
Missing (%)0.1%
Memory size49.9 KiB
Residential Vacant
4641 
Residential Improved
1083 
Commercial Vacant
 
314
RESIDENTIAL VACANT
 
165
RESIDENTIAL IMPROVED
 
39
Other values (10)
 
121
ValueCountFrequency (%) 
Residential Vacant464172.9%
 
Residential Improved108317.0%
 
Commercial Vacant3144.9%
 
RESIDENTIAL VACANT1652.6%
 
RESIDENTIAL IMPROVED390.6%
 
Industrial Vacant390.6%
 
Urban Redevelopment (UR) vacant360.6%
 
Commercial Improved290.5%
 
INDUSTRIAL VACANT80.1%
 
COMMERCIAL VACANT40.1%
 
Residential vacant1< 0.1%
 
Not Classified1< 0.1%
 
Agricultural Vacant1< 0.1%
 
COMMERCIAL Vacant1< 0.1%
 
Industrial Improved1< 0.1%
 
(Missing)40.1%
 
2020-12-12T15:30:33.961133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)0.1%
2020-12-12T15:30:34.023687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length18
Mean length18.36327941
Min length3

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1621613.9%
 
e1305111.2%
 
i1183610.1%
 
n108789.3%
 
t108369.3%
 
d69155.9%
 
64355.5%
 
l61475.3%
 
R60535.2%
 
s57674.9%
 
c53774.6%
 
V52124.5%
 
m18351.6%
 
I16211.4%
 
r15341.3%
 
o14931.3%
 
v11861.0%
 
p11491.0%
 
A5720.5%
 
C5310.5%
 
E4520.4%
 
N3900.3%
 
T3890.3%
 
D2510.2%
 
L2170.2%
 
Other values (11)5760.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter9430080.7%
 
Uppercase Letter1611213.8%
 
Space Separator64355.5%
 
Open Punctuation36< 0.1%
 
Close Punctuation36< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R605337.6%
 
V521232.3%
 
I162110.1%
 
A5723.6%
 
C5313.3%
 
E4522.8%
 
N3902.4%
 
T3892.4%
 
D2511.6%
 
L2171.3%
 
S2121.3%
 
U800.5%
 
M490.3%
 
O440.3%
 
P390.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1621617.2%
 
e1305113.8%
 
i1183612.6%
 
n1087811.5%
 
t1083611.5%
 
d69157.3%
 
l61476.5%
 
s57676.1%
 
c53775.7%
 
m18351.9%
 
r15341.6%
 
o14931.6%
 
v11861.3%
 
p11491.2%
 
u42< 0.1%
 
b36< 0.1%
 
f1< 0.1%
 
g1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6435100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(36100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)36100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin11041294.4%
 
Common65075.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1621614.7%
 
e1305111.8%
 
i1183610.7%
 
n108789.9%
 
t108369.8%
 
d69156.3%
 
l61475.6%
 
R60535.5%
 
s57675.2%
 
c53774.9%
 
V52124.7%
 
m18351.7%
 
I16211.5%
 
r15341.4%
 
o14931.4%
 
v11861.1%
 
p11491.0%
 
A5720.5%
 
C5310.5%
 
E4520.4%
 
N3900.4%
 
T3890.4%
 
D2510.2%
 
L2170.2%
 
S2120.2%
 
Other values (8)2920.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
643598.9%
 
(360.6%
 
)360.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII116919100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1621613.9%
 
e1305111.2%
 
i1183610.1%
 
n108789.3%
 
t108369.3%
 
d69155.9%
 
64355.5%
 
l61475.3%
 
R60535.2%
 
s57674.9%
 
c53774.6%
 
V52124.5%
 
m18351.6%
 
I16211.4%
 
r15341.3%
 
o14931.3%
 
v11861.0%
 
p11491.0%
 
A5720.5%
 
C5310.5%
 
E4520.4%
 
N3900.3%
 
T3890.3%
 
D2510.2%
 
L2170.2%
 
Other values (11)5760.5%
 

Property Status
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
Acquired
3117 
Disposed
2563 
Reserved
357 
ACQUIRED
 
210
Disposition in Process
 
58
Other values (4)
 
62
ValueCountFrequency (%) 
Acquired311749.0%
 
Disposed256340.3%
 
Reserved3575.6%
 
ACQUIRED2103.3%
 
Disposition in Process580.9%
 
Target330.5%
 
Not owned220.3%
 
Evaluation in Process60.1%
 
Retired1< 0.1%
 
2020-12-12T15:30:34.090745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T15:30:34.137785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:34.214852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length8
Mean length8.132715565
Min length6

Overview of Unicode Properties

Unique unicode characters29
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e687213.3%
 
d606011.7%
 
i592511.4%
 
s572711.1%
 
r35726.9%
 
A33276.4%
 
c31816.1%
 
u31236.0%
 
q31176.0%
 
D28315.5%
 
o27935.4%
 
p26215.1%
 
R5681.1%
 
v3630.7%
 
E2160.4%
 
C2100.4%
 
Q2100.4%
 
U2100.4%
 
I2100.4%
 
n1500.3%
 
1500.3%
 
t1200.2%
 
P640.1%
 
a450.1%
 
T330.1%
 
Other values (4)830.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4373084.5%
 
Uppercase Letter790115.3%
 
Space Separator1500.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A332742.1%
 
D283135.8%
 
R5687.2%
 
E2162.7%
 
C2102.7%
 
Q2102.7%
 
U2102.7%
 
I2102.7%
 
P640.8%
 
T330.4%
 
N220.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e687215.7%
 
d606013.9%
 
i592513.5%
 
s572713.1%
 
r35728.2%
 
c31817.3%
 
u31237.1%
 
q31177.1%
 
o27936.4%
 
p26216.0%
 
v3630.8%
 
n1500.3%
 
t1200.3%
 
a450.1%
 
g330.1%
 
w220.1%
 
l6< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
150100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5163199.7%
 
Common1500.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e687213.3%
 
d606011.7%
 
i592511.5%
 
s572711.1%
 
r35726.9%
 
A33276.4%
 
c31816.2%
 
u31236.0%
 
q31176.0%
 
D28315.5%
 
o27935.4%
 
p26215.1%
 
R5681.1%
 
v3630.7%
 
E2160.4%
 
C2100.4%
 
Q2100.4%
 
U2100.4%
 
I2100.4%
 
n1500.3%
 
t1200.2%
 
P640.1%
 
a450.1%
 
T330.1%
 
g330.1%
 
Other values (3)500.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
150100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII51781100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e687213.3%
 
d606011.7%
 
i592511.4%
 
s572711.1%
 
r35726.9%
 
A33276.4%
 
c31816.1%
 
u31236.0%
 
q31176.0%
 
D28315.5%
 
o27935.4%
 
p26215.1%
 
R5681.1%
 
v3630.7%
 
E2160.4%
 
C2100.4%
 
Q2100.4%
 
U2100.4%
 
I2100.4%
 
n1500.3%
 
1500.3%
 
t1200.2%
 
P640.1%
 
a450.1%
 
T330.1%
 
Other values (4)830.2%
 

Inventory Type
Categorical

Distinct4
Distinct (%)0.1%
Missing3
Missing (%)< 0.1%
Memory size49.9 KiB
Land Bank
5263 
KCMHA
956 
LAND BANK
 
134
Private
 
11
ValueCountFrequency (%) 
Land Bank526382.7%
 
KCMHA95615.0%
 
LAND BANK1342.1%
 
Private110.2%
 
(Missing)3< 0.1%
 
2020-12-12T15:30:34.283911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:30:34.325447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:34.378993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length8.393120779
Min length3

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1054019.7%
 
n1053219.7%
 
L539710.1%
 
539710.1%
 
B539710.1%
 
d52639.8%
 
k52639.8%
 
A12242.3%
 
K10902.0%
 
C9561.8%
 
M9561.8%
 
H9561.8%
 
N2680.5%
 
D1340.3%
 
P11< 0.1%
 
r11< 0.1%
 
i11< 0.1%
 
v11< 0.1%
 
t11< 0.1%
 
e11< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3165359.2%
 
Uppercase Letter1638930.7%
 
Space Separator539710.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L539732.9%
 
B539732.9%
 
A12247.5%
 
K10906.7%
 
C9565.8%
 
M9565.8%
 
H9565.8%
 
N2681.6%
 
D1340.8%
 
P110.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1054033.3%
 
n1053233.3%
 
d526316.6%
 
k526316.6%
 
r11< 0.1%
 
i11< 0.1%
 
v11< 0.1%
 
t11< 0.1%
 
e11< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
5397100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4804289.9%
 
Common539710.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1054021.9%
 
n1053221.9%
 
L539711.2%
 
B539711.2%
 
d526311.0%
 
k526311.0%
 
A12242.5%
 
K10902.3%
 
C9562.0%
 
M9562.0%
 
H9562.0%
 
N2680.6%
 
D1340.3%
 
P11< 0.1%
 
r11< 0.1%
 
i11< 0.1%
 
v11< 0.1%
 
t11< 0.1%
 
e11< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
5397100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII53439100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1054019.7%
 
n1053219.7%
 
L539710.1%
 
539710.1%
 
B539710.1%
 
d52639.8%
 
k52639.8%
 
A12242.3%
 
K10902.0%
 
C9561.8%
 
M9561.8%
 
H9561.8%
 
N2680.5%
 
D1340.3%
 
P11< 0.1%
 
r11< 0.1%
 
i11< 0.1%
 
v11< 0.1%
 
t11< 0.1%
 
e11< 0.1%
 

Zoned As
Categorical

MISSING

Distinct22
Distinct (%)0.4%
Missing95
Missing (%)1.5%
Memory size49.9 KiB
R-2.5
3809 
R-6
624 
R-1.5
612 
R-5
 
361
B3-2
 
304
Other values (17)
562 
ValueCountFrequency (%) 
R-2.5380959.8%
 
R-66249.8%
 
R-1.56129.6%
 
R-53615.7%
 
B3-23044.8%
 
R-7.52443.8%
 
B1-1731.1%
 
M1-5711.1%
 
Multiple-Refer to City Map470.7%
 
UR290.5%
 
B4-5240.4%
 
B4-2230.4%
 
R-80120.2%
 
R-0.5110.2%
 
M3-570.1%
 
R-0.370.1%
 
B2-250.1%
 
R2b40.1%
 
R1a2< 0.1%
 
C11< 0.1%
 
R1b1< 0.1%
 
R41< 0.1%
 
(Missing)951.5%
 
2020-12-12T15:30:34.453557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-12-12T15:30:34.522116image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length5
Mean length4.717449348
Min length2

Overview of Unicode Properties

Unique unicode characters30
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
-623420.8%
 
R576419.2%
 
5513917.1%
 
.468315.6%
 
2415013.8%
 
18332.8%
 
66242.1%
 
B4291.4%
 
33181.1%
 
72440.8%
 
n1900.6%
 
M1720.6%
 
a1440.5%
 
t1410.5%
 
e1410.5%
 
1410.5%
 
l940.3%
 
i940.3%
 
p940.3%
 
C480.2%
 
4480.2%
 
u470.2%
 
f470.2%
 
r470.2%
 
o470.2%
 
Other values (5)1230.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1139837.9%
 
Uppercase Letter644221.4%
 
Dash Punctuation623420.8%
 
Other Punctuation468315.6%
 
Lowercase Letter11383.8%
 
Space Separator1410.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R576489.5%
 
B4296.7%
 
M1722.7%
 
C480.7%
 
U290.5%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6234100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
5513945.1%
 
2415036.4%
 
18337.3%
 
66245.5%
 
33182.8%
 
72442.1%
 
4480.4%
 
0300.3%
 
8120.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.4683100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n19016.7%
 
a14412.7%
 
t14112.4%
 
e14112.4%
 
l948.3%
 
i948.3%
 
p948.3%
 
u474.1%
 
f474.1%
 
r474.1%
 
o474.1%
 
y474.1%
 
b50.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
141100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2245674.8%
 
Latin758025.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R576476.0%
 
B4295.7%
 
n1902.5%
 
M1722.3%
 
a1441.9%
 
t1411.9%
 
e1411.9%
 
l941.2%
 
i941.2%
 
p941.2%
 
C480.6%
 
u470.6%
 
f470.6%
 
r470.6%
 
o470.6%
 
y470.6%
 
U290.4%
 
b50.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
-623427.8%
 
5513922.9%
 
.468320.9%
 
2415018.5%
 
18333.7%
 
66242.8%
 
33181.4%
 
72441.1%
 
1410.6%
 
4480.2%
 
0300.1%
 
8120.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII30036100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
-623420.8%
 
R576419.2%
 
5513917.1%
 
.468315.6%
 
2415013.8%
 
18332.8%
 
66242.1%
 
B4291.4%
 
33181.1%
 
72440.8%
 
n1900.6%
 
M1720.6%
 
a1440.5%
 
t1410.5%
 
e1410.5%
 
1410.5%
 
l940.3%
 
i940.3%
 
p940.3%
 
C480.2%
 
4480.2%
 
u470.2%
 
f470.2%
 
r470.2%
 
o470.2%
 
Other values (5)1230.4%
 

Address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct6291
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
No Address Assigned By City
 
42
NO ADDRESS ASSIGNED BY CITY
 
13
NO ADDRESS
 
3
0 NO ADDRESS
 
3
3925 Cleveland Ave
 
2
Other values (6286)
6304 
ValueCountFrequency (%) 
No Address Assigned By City420.7%
 
NO ADDRESS ASSIGNED BY CITY130.2%
 
NO ADDRESS3< 0.1%
 
0 NO ADDRESS3< 0.1%
 
3925 Cleveland Ave2< 0.1%
 
3214 E 7th St2< 0.1%
 
916 Van Brunt Blvd2< 0.1%
 
2611 Indiana Ave2< 0.1%
 
4409 Brooklyn2< 0.1%
 
5001 CHESTNUT AVE2< 0.1%
 
3836 Myrtle2< 0.1%
 
2217 MONTGALL AVE2< 0.1%
 
3211 Brighton Ave2< 0.1%
 
No Address Assigned2< 0.1%
 
5609 E 33rd Ter2< 0.1%
 
4433 Forest2< 0.1%
 
8305 Holmes Rd2< 0.1%
 
NO ADDRESS ASSIGNED BY CITY, KANSAS CITY, MO2< 0.1%
 
6542 Charlotte St2< 0.1%
 
3814 E 69th Ter2< 0.1%
 
4119 Tracy2< 0.1%
 
8101 E 55th St2< 0.1%
 
6231 E 9TH ST2< 0.1%
 
5607 E 36th St1< 0.1%
 
2010 Montgall Ave1< 0.1%
 
Other values (6266)626698.4%
 
2020-12-12T15:30:34.605688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6268 ?
Unique (%)98.4%
2020-12-12T15:30:34.689260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length44
Median length15
Mean length15.63640647
Min length9

Overview of Unicode Properties

Unique unicode characters67
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1490315.0%
 
A55595.6%
 
E51465.2%
 
e46174.6%
 
245244.5%
 
139504.0%
 
337823.8%
 
034443.5%
 
432633.3%
 
t28352.8%
 
525742.6%
 
S24962.5%
 
v23262.3%
 
T22632.3%
 
V20952.1%
 
n20192.0%
 
619111.9%
 
N16101.6%
 
o15641.6%
 
r15591.6%
 
715461.6%
 
815181.5%
 
l14501.5%
 
O14431.4%
 
914291.4%
 
Other values (42)1973119.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter3142931.6%
 
Decimal Number2794128.1%
 
Lowercase Letter2510225.2%
 
Space Separator1490315.0%
 
Other Punctuation870.1%
 
Open Punctuation40< 0.1%
 
Close Punctuation40< 0.1%
 
Dash Punctuation15< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2452416.2%
 
1395014.1%
 
3378213.5%
 
0344412.3%
 
4326311.7%
 
525749.2%
 
619116.8%
 
715465.5%
 
815185.4%
 
914295.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
14903100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A555917.7%
 
E514616.4%
 
S24967.9%
 
T22637.2%
 
V20956.7%
 
N16105.1%
 
O14434.6%
 
L13414.3%
 
R11823.8%
 
H10463.3%
 
C9673.1%
 
B9653.1%
 
I8412.7%
 
D7762.5%
 
M6332.0%
 
W6121.9%
 
P5961.9%
 
G5381.7%
 
K4081.3%
 
Y3271.0%
 
U2790.9%
 
F1890.6%
 
J850.3%
 
Q280.1%
 
X2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e461718.4%
 
t283511.3%
 
v23269.3%
 
n20198.0%
 
o15646.2%
 
r15596.2%
 
l14505.8%
 
a13755.5%
 
s12695.1%
 
h12014.8%
 
d9183.7%
 
i8513.4%
 
g5982.4%
 
y4912.0%
 
u4541.8%
 
p3801.5%
 
c3041.2%
 
k3011.2%
 
w2310.9%
 
m1260.5%
 
b980.4%
 
f880.4%
 
x370.1%
 
z10< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.8193.1%
 
,44.6%
 
/22.3%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(40100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)40100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-15100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5653156.8%
 
Common4302643.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
1490334.6%
 
2452410.5%
 
139509.2%
 
337828.8%
 
034448.0%
 
432637.6%
 
525746.0%
 
619114.4%
 
715463.6%
 
815183.5%
 
914293.3%
 
.810.2%
 
(400.1%
 
)400.1%
 
-15< 0.1%
 
,4< 0.1%
 
/2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A55599.8%
 
E51469.1%
 
e46178.2%
 
t28355.0%
 
S24964.4%
 
v23264.1%
 
T22634.0%
 
V20953.7%
 
n20193.6%
 
N16102.8%
 
o15642.8%
 
r15592.8%
 
l14502.6%
 
O14432.6%
 
a13752.4%
 
L13412.4%
 
s12692.2%
 
h12012.1%
 
R11822.1%
 
H10461.9%
 
C9671.7%
 
B9651.7%
 
d9181.6%
 
i8511.5%
 
I8411.5%
 
Other values (25)759313.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII99557100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1490315.0%
 
A55595.6%
 
E51465.2%
 
e46174.6%
 
245244.5%
 
139504.0%
 
337823.8%
 
034443.5%
 
432633.3%
 
t28352.8%
 
525742.6%
 
S24962.5%
 
v23262.3%
 
T22632.3%
 
V20952.1%
 
n20192.0%
 
619111.9%
 
N16101.6%
 
o15641.6%
 
r15591.6%
 
715461.6%
 
815181.5%
 
l14501.5%
 
O14431.4%
 
914291.4%
 
Other values (42)1973119.8%
 

City
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
Kansas City
4677 
KANSAS CITY
1679 
kansas city
 
6
kansas City
 
4
Kansas city
 
1
ValueCountFrequency (%) 
Kansas City467773.5%
 
KANSAS CITY167926.4%
 
kansas city60.1%
 
kansas City40.1%
 
Kansas city1< 0.1%
 
2020-12-12T15:30:34.762323image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T15:30:34.803859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:34.859406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length11
Min length11

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a937613.4%
 
s937613.4%
 
63679.1%
 
C63609.1%
 
K63579.1%
 
n46886.7%
 
i46886.7%
 
t46886.7%
 
y46886.7%
 
A33584.8%
 
S33584.8%
 
N16792.4%
 
I16792.4%
 
T16792.4%
 
Y16792.4%
 
k10< 0.1%
 
c7< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3752153.6%
 
Uppercase Letter2614937.3%
 
Space Separator63679.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C636024.3%
 
K635724.3%
 
A335812.8%
 
S335812.8%
 
N16796.4%
 
I16796.4%
 
T16796.4%
 
Y16796.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a937625.0%
 
s937625.0%
 
n468812.5%
 
i468812.5%
 
t468812.5%
 
y468812.5%
 
k10< 0.1%
 
c7< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6367100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6367090.9%
 
Common63679.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a937614.7%
 
s937614.7%
 
C636010.0%
 
K635710.0%
 
n46887.4%
 
i46887.4%
 
t46887.4%
 
y46887.4%
 
A33585.3%
 
S33585.3%
 
N16792.6%
 
I16792.6%
 
T16792.6%
 
Y16792.6%
 
k10< 0.1%
 
c7< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
6367100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII70037100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a937613.4%
 
s937613.4%
 
63679.1%
 
C63609.1%
 
K63579.1%
 
n46886.7%
 
i46886.7%
 
t46886.7%
 
y46886.7%
 
A33584.8%
 
S33584.8%
 
N16792.4%
 
I16792.4%
 
T16792.4%
 
Y16792.4%
 
k10< 0.1%
 
c7< 0.1%
 

State
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
MO
6366 
MP
 
1
ValueCountFrequency (%) 
MO6366> 99.9%
 
MP1< 0.1%
 
2020-12-12T15:30:34.921460image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T15:30:34.962495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:35.004031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
M636750.0%
 
O636650.0%
 
P1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter12734100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M636750.0%
 
O636650.0%
 
P1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin12734100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M636750.0%
 
O636650.0%
 
P1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII12734100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
M636750.0%
 
O636650.0%
 
P1< 0.1%
 

Postal Code
Categorical

HIGH CARDINALITY

Distinct87
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
64127
1403 
64130
1102 
64128
1019 
64132
411 
64130.0
339 
Other values (82)
2093 
ValueCountFrequency (%) 
64127140322.0%
 
64130110217.3%
 
64128101916.0%
 
641324116.5%
 
64130.03395.3%
 
641092964.6%
 
641242173.4%
 
641261903.0%
 
641291572.5%
 
641251292.0%
 
641101231.9%
 
64110.01031.6%
 
64123931.5%
 
64109.0921.4%
 
64131881.4%
 
64127.0851.3%
 
64133741.2%
 
64128.0691.1%
 
99999580.9%
 
64134460.7%
 
64108450.7%
 
64132.0210.3%
 
64114200.3%
 
64138180.3%
 
64124.0160.3%
 
Other values (62)1532.4%
 
2020-12-12T15:30:35.083599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique42 ?
Unique (%)0.7%
2020-12-12T15:30:35.160165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length5
Mean length5.275482959
Min length5

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1671120.0%
 
4664019.8%
 
6653719.5%
 
2389611.6%
 
029258.7%
 
323447.0%
 
715214.5%
 
811703.5%
 
98662.6%
 
.7822.3%
 
51590.5%
 
-380.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3276997.6%
 
Other Punctuation7822.3%
 
Dash Punctuation380.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1671120.5%
 
4664020.3%
 
6653719.9%
 
2389611.9%
 
029258.9%
 
323447.2%
 
715214.6%
 
811703.6%
 
98662.6%
 
51590.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.782100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-38100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common33589100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1671120.0%
 
4664019.8%
 
6653719.5%
 
2389611.6%
 
029258.7%
 
323447.0%
 
715214.5%
 
811703.5%
 
98662.6%
 
.7822.3%
 
51590.5%
 
-380.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII33589100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1671120.0%
 
4664019.8%
 
6653719.5%
 
2389611.6%
 
029258.7%
 
323447.0%
 
715214.5%
 
811703.5%
 
98662.6%
 
.7822.3%
 
51590.5%
 
-380.1%
 

Latitude
Real number (ℝ≥0)

MISSING

Distinct5681
Distinct (%)91.2%
Missing137
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean39.05753325
Minimum38.86173908
Maximum39.192295
Zeros0
Zeros (%)0.0%
Memory size49.9 KiB
2020-12-12T15:30:35.230225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum38.86173908
5-th percentile38.98429198
Q139.03703386
median39.06463891
Q339.082722
95-th percentile39.1043364
Maximum39.192295
Range0.33055592
Interquartile range (IQR)0.045688145

Descriptive statistics

Standard deviation0.03629081099
Coefficient of variation (CV)0.0009291629034
Kurtosis1.192351434
Mean39.05753325
Median Absolute Deviation (MAD)0.0202105
Skewness-0.9991157605
Sum243328.4321
Variance0.001317022963
MonotocityNot monotonic
2020-12-12T15:30:35.313297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
39.002445140.2%
 
39.10295170.1%
 
39.01854360.1%
 
39.08124250.1%
 
39.0765850.1%
 
39.0859650.1%
 
39.08105850.1%
 
39.07899950.1%
 
39.0793840.1%
 
39.06415940.1%
 
39.08668140.1%
 
39.06388840.1%
 
39.06444940.1%
 
39.07355940.1%
 
39.07661840.1%
 
39.08076940.1%
 
39.088240.1%
 
39.07656140.1%
 
39.08063940.1%
 
39.0772140.1%
 
39.08272240.1%
 
39.0711940.1%
 
39.071773< 0.1%
 
39.0808983< 0.1%
 
39.0851593< 0.1%
 
Other values (5656)611396.0%
 
(Missing)1372.2%
 
ValueCountFrequency (%) 
38.861739081< 0.1%
 
38.8790071< 0.1%
 
38.884141< 0.1%
 
38.907935341< 0.1%
 
38.9092711< 0.1%
 
38.911561< 0.1%
 
38.91211< 0.1%
 
38.9126431< 0.1%
 
38.914013061< 0.1%
 
38.915335871< 0.1%
 
ValueCountFrequency (%) 
39.1922951< 0.1%
 
39.178521< 0.1%
 
39.1767811< 0.1%
 
39.175221< 0.1%
 
39.172231< 0.1%
 
39.1375031< 0.1%
 
39.1334991< 0.1%
 
39.1273881< 0.1%
 
39.124552931< 0.1%
 
39.12375791< 0.1%
 

Longitude
Real number (ℝ)

MISSING

Distinct5603
Distinct (%)89.9%
Missing137
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean-94.54013682
Minimum-94.633362
Maximum-94.39728
Zeros0
Zeros (%)0.0%
Memory size49.9 KiB
2020-12-12T15:30:35.401373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-94.633362
5-th percentile-94.56630303
Q1-94.55430754
median-94.5436873
Q3-94.52875125
95-th percentile-94.5020423
Maximum-94.39728
Range0.236082
Interquartile range (IQR)0.02555629

Descriptive statistics

Standard deviation0.02075101665
Coefficient of variation (CV)-0.0002194942524
Kurtosis1.872003349
Mean-94.54013682
Median Absolute Deviation (MAD)0.012439335
Skewness0.818712635
Sum-588985.0524
Variance0.0004306046919
MonotocityNot monotonic
2020-12-12T15:30:35.486446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-94.553169150.2%
 
-94.53078560.1%
 
-94.5433550.1%
 
-94.50676750.1%
 
-94.54540250.1%
 
-94.55342950.1%
 
-94.518640.1%
 
-94.55055240.1%
 
-94.55021740.1%
 
-94.54306840.1%
 
-94.58306140.1%
 
-94.5504340.1%
 
-94.51735740.1%
 
-94.52990740.1%
 
-94.5545240.1%
 
-94.54872140.1%
 
-94.55113240.1%
 
-94.519640.1%
 
-94.5431940.1%
 
-94.550440.1%
 
-94.53986440.1%
 
-94.53951340.1%
 
-94.54547940.1%
 
-94.5522543< 0.1%
 
-94.5538643< 0.1%
 
Other values (5578)611596.0%
 
(Missing)1372.2%
 
ValueCountFrequency (%) 
-94.6333621< 0.1%
 
-94.6060291< 0.1%
 
-94.6053011< 0.1%
 
-94.60368021< 0.1%
 
-94.6010441< 0.1%
 
-94.6009291< 0.1%
 
-94.599159881< 0.1%
 
-94.599151951< 0.1%
 
-94.599140341< 0.1%
 
-94.598746021< 0.1%
 
ValueCountFrequency (%) 
-94.397281< 0.1%
 
-94.4073411< 0.1%
 
-94.4199681< 0.1%
 
-94.4307711< 0.1%
 
-94.4347381< 0.1%
 
-94.440751< 0.1%
 
-94.4426221< 0.1%
 
-94.452730521< 0.1%
 
-94.4546891< 0.1%
 
-94.45772831< 0.1%
 

County
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
Jackson
5434 
JACKSON
929 
Clay
 
4
ValueCountFrequency (%) 
Jackson543485.3%
 
JACKSON92914.6%
 
Clay40.1%
 
2020-12-12T15:30:35.564513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:30:35.606549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:35.658093image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length6.998115282
Min length4

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
J636314.3%
 
a543812.2%
 
c543412.2%
 
k543412.2%
 
s543412.2%
 
o543412.2%
 
n543412.2%
 
C9332.1%
 
A9292.1%
 
K9292.1%
 
S9292.1%
 
O9292.1%
 
N9292.1%
 
l4< 0.1%
 
y4< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3261673.2%
 
Uppercase Letter1194126.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J636353.3%
 
C9337.8%
 
A9297.8%
 
K9297.8%
 
S9297.8%
 
O9297.8%
 
N9297.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a543816.7%
 
c543416.7%
 
k543416.7%
 
s543416.7%
 
o543416.7%
 
n543416.7%
 
l4< 0.1%
 
y4< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin44557100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
J636314.3%
 
a543812.2%
 
c543412.2%
 
k543412.2%
 
s543412.2%
 
o543412.2%
 
n543412.2%
 
C9332.1%
 
A9292.1%
 
K9292.1%
 
S9292.1%
 
O9292.1%
 
N9292.1%
 
l4< 0.1%
 
y4< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII44557100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
J636314.3%
 
a543812.2%
 
c543412.2%
 
k543412.2%
 
s543412.2%
 
o543412.2%
 
n543412.2%
 
C9332.1%
 
A9292.1%
 
K9292.1%
 
S9292.1%
 
O9292.1%
 
N9292.1%
 
l4< 0.1%
 
y4< 0.1%
 

Neighborhood
Categorical

HIGH CARDINALITY

Distinct132
Distinct (%)2.1%
Missing32
Missing (%)0.5%
Memory size49.9 KiB
Washington Wheatley
470 
East Community Team South
 
446
Blue Hills
 
305
East Community Team North
 
285
Oak Park Northwest
 
269
Other values (127)
4560 
ValueCountFrequency (%) 
Washington Wheatley4707.4%
 
East Community Team South4467.0%
 
Blue Hills3054.8%
 
East Community Team North2854.5%
 
Oak Park Northwest2694.2%
 
North Town Fork Creek2233.5%
 
Wendell Phillips2033.2%
 
Key Coalition1903.0%
 
Lykins1862.9%
 
Ivanhoe Southeast1772.8%
 
South Blue Valley1752.7%
 
Oak Park Southwest1722.7%
 
Swope Parkway-Elmwood1692.7%
 
Knoches Park1692.7%
 
Vineyard Northwest1452.3%
 
Dunbar1231.9%
 
Marlborough Heights/Marlborough Pride1131.8%
 
Ivanhoe Southwest1131.8%
 
Self Help Neighborhood Council1081.7%
 
Sheffield1061.7%
 
Santa Fe981.5%
 
East Blue Valley981.5%
 
Ivanhoe Northeast911.4%
 
Oak Park Southeast881.4%
 
Ingleside841.3%
 
Other values (107)172927.2%
 
2020-12-12T15:30:35.739163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique15 ?
Unique (%)0.2%
2020-12-12T15:30:35.824237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length42
Median length17
Mean length17.49363908
Min length2

Overview of Unicode Properties

Unique unicode characters54
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
100819.1%
 
e93788.4%
 
a82037.4%
 
o81697.3%
 
t81127.3%
 
n56055.0%
 
l55555.0%
 
h53154.8%
 
s49314.4%
 
r49274.4%
 
i44844.0%
 
u37053.3%
 
m26112.3%
 
k25402.3%
 
y25062.2%
 
d20541.8%
 
S19451.7%
 
w18991.7%
 
P17391.6%
 
C14381.3%
 
N14361.3%
 
g14181.3%
 
E14101.3%
 
W13901.2%
 
T10811.0%
 
Other values (29)94508.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter8416375.6%
 
Uppercase Letter1669615.0%
 
Space Separator100819.1%
 
Dash Punctuation2510.2%
 
Other Punctuation1130.1%
 
Decimal Number780.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S194511.6%
 
P173910.4%
 
C14388.6%
 
N14368.6%
 
E14108.4%
 
W13908.3%
 
T10816.5%
 
B9425.6%
 
H8625.2%
 
I7014.2%
 
M6413.8%
 
V6353.8%
 
O6253.7%
 
F5183.1%
 
K3612.2%
 
L3031.8%
 
R2121.3%
 
A2011.2%
 
D1330.8%
 
G1130.7%
 
U100.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e937811.1%
 
a82039.7%
 
o81699.7%
 
t81129.6%
 
n56056.7%
 
l55556.6%
 
h53156.3%
 
s49315.9%
 
r49275.9%
 
i44845.3%
 
u37054.4%
 
m26113.1%
 
k25403.0%
 
y25063.0%
 
d20542.4%
 
w18992.3%
 
g14181.7%
 
p6550.8%
 
b6270.7%
 
v5160.6%
 
c5010.6%
 
f3240.4%
 
x530.1%
 
z480.1%
 
q27< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
10081100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-251100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/113100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
72430.8%
 
61823.1%
 
41215.4%
 
91215.4%
 
31215.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin10085990.6%
 
Common105239.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e93789.3%
 
a82038.1%
 
o81698.1%
 
t81128.0%
 
n56055.6%
 
l55555.5%
 
h53155.3%
 
s49314.9%
 
r49274.9%
 
i44844.4%
 
u37053.7%
 
m26112.6%
 
k25402.5%
 
y25062.5%
 
d20542.0%
 
S19451.9%
 
w18991.9%
 
P17391.7%
 
C14381.4%
 
N14361.4%
 
g14181.4%
 
E14101.4%
 
W13901.4%
 
T10811.1%
 
B9420.9%
 
Other values (21)80668.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1008195.8%
 
-2512.4%
 
/1131.1%
 
7240.2%
 
6180.2%
 
4120.1%
 
9120.1%
 
3120.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII111382100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
100819.1%
 
e93788.4%
 
a82037.4%
 
o81697.3%
 
t81127.3%
 
n56055.0%
 
l55555.0%
 
h53154.8%
 
s49314.4%
 
r49274.4%
 
i44844.0%
 
u37053.3%
 
m26112.3%
 
k25402.3%
 
y25062.2%
 
d20541.8%
 
S19451.7%
 
w18991.7%
 
P17391.6%
 
C14381.3%
 
N14361.3%
 
g14181.3%
 
E14101.3%
 
W13901.2%
 
T10811.0%
 
Other values (29)94508.5%
 
Distinct8
Distinct (%)0.1%
Missing4
Missing (%)0.1%
Memory size49.9 KiB
3rd
4393 
5th
1331 
4th
 
399
6th
 
97
3RD
 
88
Other values (3)
 
55
ValueCountFrequency (%) 
3rd439369.0%
 
5th133120.9%
 
4th3996.3%
 
6th971.5%
 
3RD881.4%
 
5TH310.5%
 
4TH200.3%
 
1st40.1%
 
(Missing)40.1%
 
2020-12-12T15:30:35.900302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:30:35.943339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:36.005392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
3448123.5%
 
r439323.0%
 
d439323.0%
 
t18319.6%
 
h18279.6%
 
513627.1%
 
44192.2%
 
6970.5%
 
R880.5%
 
D880.5%
 
T510.3%
 
H510.3%
 
n8< 0.1%
 
a4< 0.1%
 
14< 0.1%
 
s4< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1246065.2%
 
Decimal Number636333.3%
 
Uppercase Letter2781.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3448170.4%
 
5136221.4%
 
44196.6%
 
6971.5%
 
140.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r439335.3%
 
d439335.3%
 
t183114.7%
 
h182714.7%
 
n80.1%
 
a4< 0.1%
 
s4< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R8831.7%
 
D8831.7%
 
T5118.3%
 
H5118.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1273866.7%
 
Common636333.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
3448170.4%
 
5136221.4%
 
44196.6%
 
6971.5%
 
140.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r439334.5%
 
d439334.5%
 
t183114.4%
 
h182714.3%
 
R880.7%
 
D880.7%
 
T510.4%
 
H510.4%
 
n80.1%
 
a4< 0.1%
 
s4< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19101100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
3448123.5%
 
r439323.0%
 
d439323.0%
 
t18319.6%
 
h18279.6%
 
513627.1%
 
44192.2%
 
6970.5%
 
R880.5%
 
D880.5%
 
T510.3%
 
H510.3%
 
n8< 0.1%
 
a4< 0.1%
 
14< 0.1%
 
s4< 0.1%
 

Sold Date
Categorical

HIGH CARDINALITY
MISSING

Distinct843
Distinct (%)30.6%
Missing3612
Missing (%)56.7%
Memory size49.9 KiB
09/13/2016 12:00:00 AM
 
84
08/28/2018 12:00:00 AM
 
42
04/10/2019 12:00:00 AM
 
41
05/06/2019 12:00:00 AM
 
22
04/16/2019 12:00:00 AM
 
21
Other values (838)
2545 
ValueCountFrequency (%) 
09/13/2016 12:00:00 AM841.3%
 
08/28/2018 12:00:00 AM420.7%
 
04/10/2019 12:00:00 AM410.6%
 
05/06/2019 12:00:00 AM220.3%
 
04/16/2019 12:00:00 AM210.3%
 
08/18/2016 12:00:00 AM200.3%
 
09/27/2017 12:00:00 AM180.3%
 
02/07/2020 12:00:00 AM170.3%
 
12/18/2017 12:00:00 AM150.2%
 
06/10/2019 12:00:00 AM150.2%
 
12/28/2017 12:00:00 AM140.2%
 
12/20/2017 12:00:00 AM140.2%
 
03/18/2014 12:00:00 AM130.2%
 
07/19/2018 12:00:00 AM130.2%
 
06/27/2018 12:00:00 AM120.2%
 
11/29/2017 12:00:00 AM120.2%
 
05/27/2014 12:00:00 AM120.2%
 
03/13/2018 12:00:00 AM120.2%
 
05/16/2018 12:00:00 AM110.2%
 
02/04/2016 12:00:00 AM110.2%
 
12/19/2019 12:00:00 AM110.2%
 
09/18/2015 12:00:00 AM100.2%
 
04/10/2017 12:00:00 AM100.2%
 
09/25/2017 12:00:00 AM100.2%
 
01/05/2016 12:00:00 AM100.2%
 
Other values (818)228535.9%
 
(Missing)361256.7%
 
2020-12-12T15:30:36.079957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique282 ?
Unique (%)10.2%
2020-12-12T15:30:36.156022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length3
Mean length11.22129731
Min length3

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
01713924.0%
 
1777310.9%
 
n722410.1%
 
270749.9%
 
/55107.7%
 
55107.7%
 
:55107.7%
 
a36125.1%
 
A27553.9%
 
M27553.9%
 
811661.6%
 
711231.6%
 
69851.4%
 
99721.4%
 
58611.2%
 
47671.1%
 
37101.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3857054.0%
 
Other Punctuation1102015.4%
 
Lowercase Letter1083615.2%
 
Space Separator55107.7%
 
Uppercase Letter55107.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n722466.7%
 
a361233.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01713944.4%
 
1777320.2%
 
2707418.3%
 
811663.0%
 
711232.9%
 
69852.6%
 
99722.5%
 
58612.2%
 
47672.0%
 
37101.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/551050.0%
 
:551050.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
5510100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A275550.0%
 
M275550.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common5510077.1%
 
Latin1634622.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n722444.2%
 
a361222.1%
 
A275516.9%
 
M275516.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
01713931.1%
 
1777314.1%
 
2707412.8%
 
/551010.0%
 
551010.0%
 
:551010.0%
 
811662.1%
 
711232.0%
 
69851.8%
 
99721.8%
 
58611.6%
 
47671.4%
 
37101.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII71446100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
01713924.0%
 
1777310.9%
 
n722410.1%
 
270749.9%
 
/55107.7%
 
55107.7%
 
:55107.7%
 
a36125.1%
 
A27553.9%
 
M27553.9%
 
811661.6%
 
711231.6%
 
69851.4%
 
99721.4%
 
58611.2%
 
47671.1%
 
37101.0%
 

School District
Categorical

Distinct14
Distinct (%)0.2%
Missing46
Missing (%)0.7%
Memory size49.9 KiB
KANSAS CITY MISSOURI 110
3011 
Kansas City Missouri 110
2815 
Center 120
 
162
CENTER 120
 
91
RAYTOWN 150
 
74
Other values (9)
 
168
ValueCountFrequency (%) 
KANSAS CITY MISSOURI 110301147.3%
 
Kansas City Missouri 110281544.2%
 
Center 1201622.5%
 
CENTER 120911.4%
 
RAYTOWN 150741.2%
 
Raytown 150741.2%
 
Hickman Mills 140440.7%
 
HICKMAN MILLS 140380.6%
 
Grandview 13050.1%
 
NORTH KANSAS CITY 2502< 0.1%
 
Independence 1602< 0.1%
 
GRANDVIEW 1301< 0.1%
 
Lee's Summit 1901< 0.1%
 
North Kansas City 2501< 0.1%
 
(Missing)460.7%
 
2020-12-12T15:30:36.229585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-12-12T15:30:36.294641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length24
Mean length22.88408984
Min length3

Overview of Unicode Properties

Unique unicode characters48
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1806212.4%
 
1121448.3%
 
S120878.3%
 
s113077.8%
 
I91146.3%
 
i85405.9%
 
063214.3%
 
A61394.2%
 
C61204.2%
 
M59464.1%
 
K58674.0%
 
a58014.0%
 
R32532.2%
 
N32202.2%
 
n31992.2%
 
T31802.2%
 
Y30872.1%
 
O30872.1%
 
t30542.1%
 
U30112.1%
 
r29832.0%
 
y28902.0%
 
o28902.0%
 
u28161.9%
 
e3390.2%
 
Other values (23)12460.9%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter6453844.3%
 
Lowercase Letter4413930.3%
 
Decimal Number1896313.0%
 
Space Separator1806212.4%
 
Other Punctuation1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S1208718.7%
 
I911414.1%
 
A61399.5%
 
C61209.5%
 
M59469.2%
 
K58679.1%
 
R32535.0%
 
N32205.0%
 
T31804.9%
 
Y30874.8%
 
O30874.8%
 
U30114.7%
 
E1830.3%
 
H840.1%
 
L770.1%
 
W750.1%
 
G6< 0.1%
 
D1< 0.1%
 
V1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
s1130725.6%
 
i854019.3%
 
a580113.1%
 
n31997.2%
 
t30546.9%
 
r29836.8%
 
y28906.5%
 
o28906.5%
 
u28166.4%
 
e3390.8%
 
l880.2%
 
w790.2%
 
c460.1%
 
m460.1%
 
k440.1%
 
d9< 0.1%
 
v5< 0.1%
 
p2< 0.1%
 
h1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
18062100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11214464.0%
 
0632133.3%
 
22561.3%
 
51510.8%
 
4820.4%
 
36< 0.1%
 
62< 0.1%
 
91< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin10867774.6%
 
Common3702625.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
S1208711.1%
 
s1130710.4%
 
I91148.4%
 
i85407.9%
 
A61395.6%
 
C61205.6%
 
M59465.5%
 
K58675.4%
 
a58015.3%
 
R32533.0%
 
N32203.0%
 
n31992.9%
 
T31802.9%
 
Y30872.8%
 
O30872.8%
 
t30542.8%
 
U30112.8%
 
r29832.7%
 
y28902.7%
 
o28902.7%
 
u28162.6%
 
e3390.3%
 
E1830.2%
 
l880.1%
 
H840.1%
 
Other values (13)3920.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
1806248.8%
 
11214432.8%
 
0632117.1%
 
22560.7%
 
51510.4%
 
4820.2%
 
36< 0.1%
 
62< 0.1%
 
'1< 0.1%
 
91< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII145703100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1806212.4%
 
1121448.3%
 
S120878.3%
 
s113077.8%
 
I91146.3%
 
i85405.9%
 
063214.3%
 
A61394.2%
 
C61204.2%
 
M59464.1%
 
K58674.0%
 
a58014.0%
 
R32532.2%
 
N32202.2%
 
n31992.2%
 
T31802.2%
 
Y30872.1%
 
O30872.1%
 
t30542.1%
 
U30112.1%
 
r29832.0%
 
y28902.0%
 
o28902.0%
 
u28161.9%
 
e3390.2%
 
Other values (23)12460.9%
 

Potential Use
Categorical

HIGH CARDINALITY
MISSING

Distinct78
Distinct (%)1.9%
Missing2162
Missing (%)34.0%
Memory size49.9 KiB
Side Yard
1234 
Affordable Housing
922 
Assemblage
817 
Public Purpose
279 
Infill construction
207 
Other values (73)
746 
ValueCountFrequency (%) 
Side Yard123419.4%
 
Affordable Housing92214.5%
 
Assemblage81712.8%
 
Public Purpose2794.4%
 
Infill construction2073.3%
 
Assemblage, Side Yard1081.7%
 
Urban Agriculture861.4%
 
Affordable Housing, Side Yard661.0%
 
Assemblage, Public Purpose651.0%
 
Commercial Use560.9%
 
Assemblage, Infill construction550.9%
 
Side Yard, Urban Agriculture400.6%
 
Assemblage, Urban Agriculture270.4%
 
Assemblage, Side Yard, Urban Agriculture230.4%
 
Public Green Space150.2%
 
Infill construction, Side Yard140.2%
 
Assemblage, Infill construction, Urban Agriculture130.2%
 
Public Green Space, Public Purpose120.2%
 
Industrial Use110.2%
 
Assemblage, Infill construction, Side Yard90.1%
 
Assemblage, Infill construction, Side Yard, Urban Agriculture90.1%
 
Assemblage, Public Green Space70.1%
 
Assemblage, Commercial Use70.1%
 
Wildlife Conservation Area70.1%
 
Infill construction, Urban Agriculture70.1%
 
Other values (53)1091.7%
 
(Missing)216234.0%
 
2020-12-12T15:30:36.376212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique29 ?
Unique (%)0.7%
2020-12-12T15:30:36.460284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length93
Median length9
Mean length11.19145594
Min length3

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n67449.5%
 
a63728.9%
 
e60178.4%
 
45276.4%
 
s42606.0%
 
r42225.9%
 
d41545.8%
 
i41295.8%
 
l37355.2%
 
o32624.6%
 
b28824.0%
 
u26863.8%
 
A24633.5%
 
g24373.4%
 
f24223.4%
 
S16042.3%
 
Y15422.2%
 
c15162.1%
 
m13521.9%
 
H10271.4%
 
t9741.4%
 
P8281.2%
 
,7041.0%
 
p4450.6%
 
I3590.5%
 
Other values (6)5930.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5763580.9%
 
Uppercase Letter839011.8%
 
Space Separator45276.4%
 
Other Punctuation7041.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A246329.4%
 
S160419.1%
 
Y154218.4%
 
H102712.2%
 
P8289.9%
 
I3594.3%
 
U3534.2%
 
C1151.4%
 
G620.7%
 
W260.3%
 
R110.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n674411.7%
 
a637211.1%
 
e601710.4%
 
s42607.4%
 
r42227.3%
 
d41547.2%
 
i41297.2%
 
l37356.5%
 
o32625.7%
 
b28825.0%
 
u26864.7%
 
g24374.2%
 
f24224.2%
 
c15162.6%
 
m13522.3%
 
t9741.7%
 
p4450.8%
 
v26< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4527100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,704100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6602592.7%
 
Common52317.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n674410.2%
 
a63729.7%
 
e60179.1%
 
s42606.5%
 
r42226.4%
 
d41546.3%
 
i41296.3%
 
l37355.7%
 
o32624.9%
 
b28824.4%
 
u26864.1%
 
A24633.7%
 
g24373.7%
 
f24223.7%
 
S16042.4%
 
Y15422.3%
 
c15162.3%
 
m13522.0%
 
H10271.6%
 
t9741.5%
 
P8281.3%
 
p4450.7%
 
I3590.5%
 
U3530.5%
 
C1150.2%
 
Other values (4)1250.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
452786.5%
 
,70413.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII71256100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n67449.5%
 
a63728.9%
 
e60178.4%
 
45276.4%
 
s42606.0%
 
r42225.9%
 
d41545.8%
 
i41295.8%
 
l37355.2%
 
o32624.6%
 
b28824.0%
 
u26863.8%
 
A24633.5%
 
g24373.4%
 
f24223.4%
 
S16042.3%
 
Y15422.2%
 
c15162.1%
 
m13521.9%
 
H10271.4%
 
t9741.4%
 
P8281.2%
 
,7041.0%
 
p4450.6%
 
I3590.5%
 
Other values (6)5930.8%
 

Quiet Title
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing1086
Missing (%)17.1%
Memory size49.9 KiB
N
5273 
Y
 
8
(Missing)
1086 
ValueCountFrequency (%) 
N527382.8%
 
Y80.1%
 
(Missing)108617.1%
 
2020-12-12T15:30:36.509827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing1086
Missing (%)17.1%
Memory size49.9 KiB
N
5281 
ValueCountFrequency (%) 
N528182.9%
 
(Missing)108617.1%
 
2020-12-12T15:30:36.552363image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:30:36.590396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:36.633433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.341133972
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N528161.8%
 
n217225.4%
 
a108612.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter528161.8%
 
Lowercase Letter325838.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N5281100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n217266.7%
 
a108633.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin8539100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N528161.8%
 
n217225.4%
 
a108612.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII8539100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N528161.8%
 
n217225.4%
 
a108612.7%
 

Brush Removal Needed
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing1086
Missing (%)17.1%
Memory size49.9 KiB
N
5261 
Y
 
20
(Missing)
1086 
ValueCountFrequency (%) 
N526182.6%
 
Y200.3%
 
(Missing)108617.1%
 
2020-12-12T15:30:36.679472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Trash Removal Needed
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing1086
Missing (%)17.1%
Memory size49.9 KiB
N
5250 
Y
 
31
(Missing)
1086 
ValueCountFrequency (%) 
N525082.5%
 
Y310.5%
 
(Missing)108617.1%
 
2020-12-12T15:30:36.702993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Demo Needed
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing1085
Missing (%)17.0%
Memory size49.9 KiB
N
4757 
Y
525 
(Missing)
1085 
ValueCountFrequency (%) 
N475774.7%
 
Y5258.2%
 
(Missing)108517.0%
 
2020-12-12T15:30:36.727013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Rehab Candidate
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing1086
Missing (%)17.1%
Memory size49.9 KiB
N
5207 
Y
 
74
(Missing)
1086 
ValueCountFrequency (%) 
N520781.8%
 
Y741.2%
 
(Missing)108617.1%
 
2020-12-12T15:30:36.750033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Market Value Year
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)0.1%
Missing231
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean2016.824967
Minimum2012
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size49.9 KiB
2020-12-12T15:30:36.789067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2019
Q32019
95-th percentile2019
Maximum2020
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.003068656
Coefficient of variation (CV)0.001489008072
Kurtosis-1.23444838
Mean2016.824967
Median Absolute Deviation (MAD)0
Skewness-0.7628580961
Sum12375238
Variance9.018421352
MonotocityNot monotonic
2020-12-12T15:30:36.849619image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
2019375058.9%
 
2012140622.1%
 
20154076.4%
 
20142904.6%
 
2016841.3%
 
2020801.3%
 
2018520.8%
 
2017500.8%
 
2013170.3%
 
(Missing)2313.6%
 
ValueCountFrequency (%) 
2012140622.1%
 
2013170.3%
 
20142904.6%
 
20154076.4%
 
2016841.3%
 
2017500.8%
 
2018520.8%
 
2019375058.9%
 
2020801.3%
 
ValueCountFrequency (%) 
2020801.3%
 
2019375058.9%
 
2018520.8%
 
2017500.8%
 
2016841.3%
 
20154076.4%
 
20142904.6%
 
2013170.3%
 
2012140622.1%
 

Market Value
Real number (ℝ≥0)

SKEWED

Distinct2625
Distinct (%)41.4%
Missing19
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean10621.33431
Minimum10
Maximum2479178
Zeros0
Zeros (%)0.0%
Memory size49.9 KiB
2020-12-12T15:30:36.925184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile472.7
Q12250
median5300
Q312650
95-th percentile29176.1
Maximum2479178
Range2479168
Interquartile range (IQR)10400

Descriptive statistics

Standard deviation39172.04658
Coefficient of variation (CV)3.688053254
Kurtosis2600.632193
Mean10621.33431
Median Absolute Deviation (MAD)3795.5
Skewness44.87347723
Sum67424230.22
Variance1534449233
MonotocityNot monotonic
2020-12-12T15:30:37.010257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
17021652.6%
 
1276781.2%
 
3050611.0%
 
2500580.9%
 
2250570.9%
 
2450550.9%
 
3100540.8%
 
2700520.8%
 
2200450.7%
 
2300430.7%
 
2350420.7%
 
2650390.6%
 
2600370.6%
 
2400360.6%
 
2950340.5%
 
2100330.5%
 
2150320.5%
 
1750310.5%
 
1700300.5%
 
2900290.5%
 
2800280.4%
 
2050280.4%
 
3900270.4%
 
2550270.4%
 
3300270.4%
 
Other values (2600)520081.7%
 
ValueCountFrequency (%) 
101< 0.1%
 
121< 0.1%
 
301< 0.1%
 
372< 0.1%
 
4370.1%
 
441< 0.1%
 
502< 0.1%
 
611< 0.1%
 
711< 0.1%
 
741< 0.1%
 
ValueCountFrequency (%) 
24791781< 0.1%
 
10514001< 0.1%
 
7615001< 0.1%
 
6500001< 0.1%
 
4576591< 0.1%
 
3467761< 0.1%
 
3056001< 0.1%
 
2775581< 0.1%
 
2717691< 0.1%
 
2480101< 0.1%
 

Square Footage
Real number (ℝ≥0)

MISSING
SKEWED

Distinct6263
Distinct (%)99.9%
Missing99
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean8030.302088
Minimum5
Maximum981174
Zeros0
Zeros (%)0.0%
Memory size49.9 KiB
2020-12-12T15:30:37.095330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1990.914431
Q13902.729713
median4866.340208
Q36665.841528
95-th percentile16780.12812
Maximum981174
Range981169
Interquartile range (IQR)2763.111815

Descriptive statistics

Standard deviation25071.54424
Coefficient of variation (CV)3.122117196
Kurtosis743.7919291
Mean8030.302088
Median Absolute Deviation (MAD)1297.772552
Skewness23.66894387
Sum50333933.49
Variance628582330.4
MonotocityNot monotonic
2020-12-12T15:30:37.178902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
47652< 0.1%
 
52082< 0.1%
 
15027.82672< 0.1%
 
20146.534622< 0.1%
 
5158.9267712< 0.1%
 
4008.6808331< 0.1%
 
2148.5687851< 0.1%
 
3235.431< 0.1%
 
5224.1168751< 0.1%
 
3889.0786811< 0.1%
 
4758.8924311< 0.1%
 
3888.681< 0.1%
 
4011.7500691< 0.1%
 
5185.571< 0.1%
 
5330.2644441< 0.1%
 
5159.2461811< 0.1%
 
3750.2353121< 0.1%
 
4891.5681251< 0.1%
 
5745.5394441< 0.1%
 
4270.8989581< 0.1%
 
5141.5238541< 0.1%
 
5892.1632991< 0.1%
 
4857.0368061< 0.1%
 
6793.731< 0.1%
 
4671.6111111< 0.1%
 
Other values (6238)623898.0%
 
(Missing)991.6%
 
ValueCountFrequency (%) 
51< 0.1%
 
61< 0.1%
 
7.4783331< 0.1%
 
15.7631251< 0.1%
 
29.2826391< 0.1%
 
39.932847221< 0.1%
 
41.7006941< 0.1%
 
57.6258681< 0.1%
 
65.041251< 0.1%
 
67.857222221< 0.1%
 
ValueCountFrequency (%) 
9811741< 0.1%
 
812971.68241< 0.1%
 
810294.69221< 0.1%
 
472249.3911< 0.1%
 
363319.381< 0.1%
 
322003.37621< 0.1%
 
314887.01031< 0.1%
 
302699.59391< 0.1%
 
294535.45781< 0.1%
 
259855.03931< 0.1%
 

Acquisition Date
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct83
Distinct (%)1.3%
Missing65
Missing (%)1.0%
Memory size49.9 KiB
04/09/2013
2893 
05/31/2013
742 
03/28/2014
561 
03/01/2015
506 
04/28/2015
388 
Other values (78)
1212 
ValueCountFrequency (%) 
04/09/2013289345.4%
 
05/31/201374211.7%
 
03/28/20145618.8%
 
03/01/20155067.9%
 
04/28/20153886.1%
 
02/19/20163385.3%
 
02/23/20172534.0%
 
03/01/20181392.2%
 
02/21/2019991.6%
 
02/03/2020941.5%
 
09/02/2014611.0%
 
06/06/2014590.9%
 
04/28/2014460.7%
 
03/02/2015140.2%
 
10/02/2015120.2%
 
09/30/201380.1%
 
05/16/201750.1%
 
06/06/201640.1%
 
02/26/20183< 0.1%
 
02/20/20143< 0.1%
 
10/21/20143< 0.1%
 
09/09/20163< 0.1%
 
05/24/20172< 0.1%
 
06/02/20172< 0.1%
 
08/09/20172< 0.1%
 
Other values (58)621.0%
 
(Missing)651.0%
 
2020-12-12T15:30:37.271482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique54 ?
Unique (%)0.9%
2020-12-12T15:30:37.350550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.928537773
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
01652826.1%
 
/1260419.9%
 
2867013.7%
 
1810012.8%
 
359839.5%
 
440906.5%
 
934245.4%
 
516972.7%
 
811491.8%
 
65000.8%
 
72750.4%
 
n1300.2%
 
a650.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5041679.8%
 
Other Punctuation1260419.9%
 
Lowercase Letter1950.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01652832.8%
 
2867017.2%
 
1810016.1%
 
3598311.9%
 
440908.1%
 
934246.8%
 
516973.4%
 
811492.3%
 
65001.0%
 
72750.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/12604100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n13066.7%
 
a6533.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6302099.7%
 
Latin1950.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
01652826.2%
 
/1260420.0%
 
2867013.8%
 
1810012.9%
 
359839.5%
 
440906.5%
 
934245.4%
 
516972.7%
 
811491.8%
 
65000.8%
 
72750.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n13066.7%
 
a6533.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII63215100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
01652826.1%
 
/1260419.9%
 
2867013.7%
 
1810012.8%
 
359839.5%
 
440906.5%
 
934245.4%
 
516972.7%
 
811491.8%
 
65000.8%
 
72750.4%
 
n1300.2%
 
a650.1%
 

Foreclosure Year
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)0.3%
Missing3641
Missing (%)57.2%
Infinite0
Infinite (%)0.0%
Mean2014.038885
Minimum2012
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size49.9 KiB
2020-12-12T15:30:37.409100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12012
median2014
Q32015
95-th percentile2018
Maximum2019
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.934133588
Coefficient of variation (CV)0.0009603258422
Kurtosis-0.08844803501
Mean2014.038885
Median Absolute Deviation (MAD)1
Skewness0.8558928587
Sum5490270
Variance3.740872738
MonotocityNot monotonic
2020-12-12T15:30:37.466149image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
201274611.7%
 
20135668.9%
 
20144767.5%
 
20153435.4%
 
20162554.0%
 
20171432.2%
 
20181031.6%
 
2019941.5%
 
(Missing)364157.2%
 
ValueCountFrequency (%) 
201274611.7%
 
20135668.9%
 
20144767.5%
 
20153435.4%
 
20162554.0%
 
20171432.2%
 
20181031.6%
 
2019941.5%
 
ValueCountFrequency (%) 
2019941.5%
 
20181031.6%
 
20171432.2%
 
20162554.0%
 
20153435.4%
 
20144767.5%
 
20135668.9%
 
201274611.7%
 

Property Condition
Categorical

MISSING

Distinct6
Distinct (%)0.1%
Missing325
Missing (%)5.1%
Memory size49.9 KiB
Vacant lot or land - usable
3862 
Structure - fair condition
856 
Vacant lot or land - little or no use
789 
Structure - severely distressed
 
257
Awaiting evaluation
 
239
ValueCountFrequency (%) 
Vacant lot or land - usable386260.7%
 
Structure - fair condition85613.4%
 
Vacant lot or land - little or no use78912.4%
 
Structure - severely distressed2574.0%
 
Awaiting evaluation2393.8%
 
Structure - good condition390.6%
 
(Missing)3255.1%
 
2020-12-12T15:30:37.538212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:30:37.586253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:37.653811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length37
Median length27
Mean length26.73472593
Min length3

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
2931717.2%
 
a1971311.6%
 
l152389.0%
 
t148148.7%
 
n130097.6%
 
o129877.6%
 
r91145.4%
 
e81164.8%
 
u71944.2%
 
c66983.9%
 
d60993.6%
 
-58033.4%
 
s56793.3%
 
V46512.7%
 
i44092.6%
 
b38622.3%
 
S11520.7%
 
f8560.5%
 
v4960.3%
 
g2780.2%
 
y2570.2%
 
A2390.1%
 
w2390.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12905875.8%
 
Space Separator2931717.2%
 
Uppercase Letter60423.5%
 
Dash Punctuation58033.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
V465177.0%
 
S115219.1%
 
A2394.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1971315.3%
 
l1523811.8%
 
t1481411.5%
 
n1300910.1%
 
o1298710.1%
 
r91147.1%
 
e81166.3%
 
u71945.6%
 
c66985.2%
 
d60994.7%
 
s56794.4%
 
i44093.4%
 
b38623.0%
 
f8560.7%
 
v4960.4%
 
g2780.2%
 
y2570.2%
 
w2390.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
29317100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-5803100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin13510079.4%
 
Common3512020.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1971314.6%
 
l1523811.3%
 
t1481411.0%
 
n130099.6%
 
o129879.6%
 
r91146.7%
 
e81166.0%
 
u71945.3%
 
c66985.0%
 
d60994.5%
 
s56794.2%
 
V46513.4%
 
i44093.3%
 
b38622.9%
 
S11520.9%
 
f8560.6%
 
v4960.4%
 
g2780.2%
 
y2570.2%
 
A2390.2%
 
w2390.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
2931783.5%
 
-580316.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII170220100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
2931717.2%
 
a1971311.6%
 
l152389.0%
 
t148148.7%
 
n130097.6%
 
o129877.6%
 
r91145.4%
 
e81164.8%
 
u71944.2%
 
c66983.9%
 
d60993.6%
 
-58033.4%
 
s56793.3%
 
V46512.7%
 
i44092.6%
 
b38622.3%
 
S11520.7%
 
f8560.5%
 
v4960.3%
 
g2780.2%
 
y2570.2%
 
A2390.1%
 
w2390.1%
 

Program
Categorical

MISSING

Distinct8
Distinct (%)1.7%
Missing5908
Missing (%)92.8%
Memory size49.9 KiB
Demo FY16
245 
Demolition FY15
99 
Demolition FY14
47 
special demo2015
46 
no access
 
14
Other values (3)
 
8
ValueCountFrequency (%) 
Demo FY162453.8%
 
Demolition FY15991.6%
 
Demolition FY14470.7%
 
special demo2015460.7%
 
no access140.2%
 
Quiet title action50.1%
 
PD training2< 0.1%
 
Compliance Monitoring1< 0.1%
 
(Missing)590892.8%
 
2020-12-12T15:30:37.722870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-12-12T15:30:37.769911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:37.843474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length3
Mean length3.630281137
Min length3

Overview of Unicode Properties

Unique unicode characters29
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n1198851.9%
 
a597625.9%
 
o6052.6%
 
e5082.2%
 
4642.0%
 
m4381.9%
 
14371.9%
 
D3931.7%
 
F3911.7%
 
Y3911.7%
 
i3601.6%
 
62451.1%
 
l1980.9%
 
t1690.7%
 
51450.6%
 
c800.3%
 
s740.3%
 
p470.2%
 
4470.2%
 
d460.2%
 
2460.2%
 
0460.2%
 
Q5< 0.1%
 
u5< 0.1%
 
r3< 0.1%
 
Other values (4)7< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2050088.7%
 
Uppercase Letter11845.1%
 
Decimal Number9664.2%
 
Space Separator4642.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1198858.5%
 
a597629.2%
 
o6053.0%
 
e5082.5%
 
m4382.1%
 
i3601.8%
 
l1981.0%
 
t1690.8%
 
c800.4%
 
s740.4%
 
p470.2%
 
d460.2%
 
u5< 0.1%
 
r3< 0.1%
 
g3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D39333.2%
 
F39133.0%
 
Y39133.0%
 
Q50.4%
 
P20.2%
 
C10.1%
 
M10.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
464100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
143745.2%
 
624525.4%
 
514515.0%
 
4474.9%
 
2464.8%
 
0464.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2168493.8%
 
Common14306.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n1198855.3%
 
a597627.6%
 
o6052.8%
 
e5082.3%
 
m4382.0%
 
D3931.8%
 
F3911.8%
 
Y3911.8%
 
i3601.7%
 
l1980.9%
 
t1690.8%
 
c800.4%
 
s740.3%
 
p470.2%
 
d460.2%
 
Q5< 0.1%
 
u5< 0.1%
 
r3< 0.1%
 
g3< 0.1%
 
P2< 0.1%
 
C1< 0.1%
 
M1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
46432.4%
 
143730.6%
 
624517.1%
 
514510.1%
 
4473.3%
 
2463.2%
 
0463.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII23114100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n1198851.9%
 
a597625.9%
 
o6052.6%
 
e5082.2%
 
4642.0%
 
m4381.9%
 
14371.9%
 
D3931.7%
 
F3911.7%
 
Y3911.7%
 
i3601.6%
 
62451.1%
 
l1980.9%
 
t1690.7%
 
51450.6%
 
c800.3%
 
s740.3%
 
p470.2%
 
4470.2%
 
d460.2%
 
2460.2%
 
0460.2%
 
Q5< 0.1%
 
u5< 0.1%
 
r3< 0.1%
 
Other values (4)7< 0.1%
 

Structure Type
Categorical

MISSING

Distinct7
Distinct (%)1.0%
Missing5632
Missing (%)88.5%
Memory size49.9 KiB
Single Family
690 
Demolished
 
14
Duplex
 
10
Commercial
 
9
Accessory
 
7
Other values (2)
 
5
ValueCountFrequency (%) 
Single Family69010.8%
 
Demolished140.2%
 
Duplex100.2%
 
Commercial90.1%
 
Accessory70.1%
 
Apartment Building3< 0.1%
 
Multi Family2< 0.1%
 
(Missing)563288.5%
 
2020-12-12T15:30:37.915536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:30:37.962577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:38.030135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length3
Mean length4.130202607
Min length3

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n1196045.5%
 
a633624.1%
 
l14205.4%
 
i14135.4%
 
e7472.8%
 
m7272.8%
 
y6992.7%
 
6952.6%
 
g6932.6%
 
F6922.6%
 
S6902.6%
 
o300.1%
 
s280.1%
 
D240.1%
 
c230.1%
 
r190.1%
 
d170.1%
 
u150.1%
 
h140.1%
 
p13< 0.1%
 
x10< 0.1%
 
A10< 0.1%
 
C9< 0.1%
 
t8< 0.1%
 
B3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2417291.9%
 
Uppercase Letter14305.4%
 
Space Separator6952.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1196049.5%
 
a633626.2%
 
l14205.9%
 
i14135.8%
 
e7473.1%
 
m7273.0%
 
y6992.9%
 
g6932.9%
 
o300.1%
 
s280.1%
 
c230.1%
 
r190.1%
 
d170.1%
 
u150.1%
 
h140.1%
 
p130.1%
 
x10< 0.1%
 
t8< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F69248.4%
 
S69048.3%
 
D241.7%
 
A100.7%
 
C90.6%
 
B30.2%
 
M20.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
695100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2560297.4%
 
Common6952.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n1196046.7%
 
a633624.7%
 
l14205.5%
 
i14135.5%
 
e7472.9%
 
m7272.8%
 
y6992.7%
 
g6932.7%
 
F6922.7%
 
S6902.7%
 
o300.1%
 
s280.1%
 
D240.1%
 
c230.1%
 
r190.1%
 
d170.1%
 
u150.1%
 
h140.1%
 
p130.1%
 
x10< 0.1%
 
A10< 0.1%
 
C9< 0.1%
 
t8< 0.1%
 
B3< 0.1%
 
M2< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
695100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26297100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n1196045.5%
 
a633624.1%
 
l14205.4%
 
i14135.4%
 
e7472.8%
 
m7272.8%
 
y6992.7%
 
6952.6%
 
g6932.6%
 
F6922.6%
 
S6902.6%
 
o300.1%
 
s280.1%
 
D240.1%
 
c230.1%
 
r190.1%
 
d170.1%
 
u150.1%
 
h140.1%
 
p13< 0.1%
 
x10< 0.1%
 
A10< 0.1%
 
C9< 0.1%
 
t8< 0.1%
 
B3< 0.1%
 

Structure Square Footage
Real number (ℝ≥0)

MISSING
SKEWED

Distinct387
Distinct (%)66.4%
Missing5784
Missing (%)90.8%
Infinite0
Infinite (%)0.0%
Mean8058.413379
Minimum400
Maximum4018517
Zeros0
Zeros (%)0.0%
Memory size49.9 KiB
2020-12-12T15:30:38.104199image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile647
Q1836
median1080
Q31395.5
95-th percentile2014.8
Maximum4018517
Range4018117
Interquartile range (IQR)559.5

Descriptive statistics

Standard deviation166382.2394
Coefficient of variation (CV)20.64702214
Kurtosis582.9911666
Mean8058.413379
Median Absolute Deviation (MAD)277
Skewness24.14511907
Sum4698055
Variance2.768304959e+10
MonotocityNot monotonic
2020-12-12T15:30:38.187770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
816110.2%
 
768110.2%
 
96080.1%
 
70470.1%
 
72070.1%
 
74860.1%
 
79260.1%
 
90250.1%
 
77050.1%
 
89650.1%
 
133640.1%
 
133440.1%
 
114040.1%
 
112040.1%
 
86440.1%
 
72840.1%
 
110040.1%
 
94040.1%
 
17403< 0.1%
 
10563< 0.1%
 
12623< 0.1%
 
13603< 0.1%
 
8883< 0.1%
 
9003< 0.1%
 
6163< 0.1%
 
Other values (362)4597.2%
 
(Missing)578490.8%
 
ValueCountFrequency (%) 
4001< 0.1%
 
4351< 0.1%
 
4751< 0.1%
 
4802< 0.1%
 
5521< 0.1%
 
5721< 0.1%
 
5741< 0.1%
 
5751< 0.1%
 
5762< 0.1%
 
5891< 0.1%
 
ValueCountFrequency (%) 
40185171< 0.1%
 
52001< 0.1%
 
32081< 0.1%
 
31201< 0.1%
 
30361< 0.1%
 
28731< 0.1%
 
25471< 0.1%
 
25061< 0.1%
 
24921< 0.1%
 
24161< 0.1%
 

Number of Bedrooms
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)0.9%
Missing5402
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean2.580310881
Minimum0
Maximum12
Zeros2
Zeros (%)< 0.1%
Memory size49.9 KiB
2020-12-12T15:30:38.258331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q33
95-th percentile4
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9710679339
Coefficient of variation (CV)0.3763375728
Kurtosis18.54907815
Mean2.580310881
Median Absolute Deviation (MAD)1
Skewness2.548382601
Sum2490
Variance0.9429729323
MonotocityNot monotonic
2020-12-12T15:30:38.317382image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
24807.5%
 
33185.0%
 
4901.4%
 
1450.7%
 
5220.3%
 
650.1%
 
02< 0.1%
 
122< 0.1%
 
71< 0.1%
 
(Missing)540284.8%
 
ValueCountFrequency (%) 
02< 0.1%
 
1450.7%
 
24807.5%
 
33185.0%
 
4901.4%
 
5220.3%
 
650.1%
 
71< 0.1%
 
122< 0.1%
 
ValueCountFrequency (%) 
122< 0.1%
 
71< 0.1%
 
650.1%
 
5220.3%
 
4901.4%
 
33185.0%
 
24807.5%
 
1450.7%
 
02< 0.1%
 

Number of Full Baths
Categorical

MISSING

Distinct4
Distinct (%)0.6%
Missing5730
Missing (%)90.0%
Memory size49.9 KiB
1
565 
2
65 
3
 
6
4
 
1
ValueCountFrequency (%) 
15658.9%
 
2651.0%
 
360.1%
 
41< 0.1%
 
(Missing)573090.0%
 
2020-12-12T15:30:38.383439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-12-12T15:30:38.424975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:38.474017image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n1146060.0%
 
a573030.0%
 
.6373.3%
 
06373.3%
 
15653.0%
 
2650.3%
 
36< 0.1%
 
41< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1719090.0%
 
Decimal Number12746.7%
 
Other Punctuation6373.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1146066.7%
 
a573033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
063750.0%
 
156544.3%
 
2655.1%
 
360.5%
 
410.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.637100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1719090.0%
 
Common191110.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n1146066.7%
 
a573033.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
.63733.3%
 
063733.3%
 
156529.6%
 
2653.4%
 
360.3%
 
410.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII19101100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n1146060.0%
 
a573030.0%
 
.6373.3%
 
06373.3%
 
15653.0%
 
2650.3%
 
36< 0.1%
 
41< 0.1%
 

Number of Half Baths
Boolean

MISSING

Distinct2
Distinct (%)18.2%
Missing6356
Missing (%)99.8%
Memory size49.9 KiB
1
 
10
0
 
1
(Missing)
6356 
ValueCountFrequency (%) 
1100.2%
 
01< 0.1%
 
(Missing)635699.8%
 
2020-12-12T15:30:38.514552image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
False
6287 
True
 
80
ValueCountFrequency (%) 
False628798.7%
 
True801.3%
 
2020-12-12T15:30:38.538072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
False
6323 
True
 
44
ValueCountFrequency (%) 
False632399.3%
 
True440.7%
 
2020-12-12T15:30:38.562092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Date evaluated
Categorical

HIGH CARDINALITY
MISSING

Distinct244
Distinct (%)17.1%
Missing4939
Missing (%)77.6%
Memory size49.9 KiB
01/09/2014
 
45
12/26/2013
 
37
03/05/2014
 
33
12/16/2014
 
27
01/02/2014
 
26
Other values (239)
1260 
ValueCountFrequency (%) 
01/09/2014450.7%
 
12/26/2013370.6%
 
03/05/2014330.5%
 
12/16/2014270.4%
 
01/02/2014260.4%
 
12/04/2015250.4%
 
03/13/2014240.4%
 
01/20/2014230.4%
 
01/07/2014230.4%
 
04/08/2015220.3%
 
12/03/2015220.3%
 
07/25/2014210.3%
 
12/10/2015210.3%
 
12/09/2015210.3%
 
03/12/2014210.3%
 
03/11/2014190.3%
 
01/30/2014190.3%
 
12/31/2013190.3%
 
01/29/2014180.3%
 
12/18/2014170.3%
 
03/06/2014170.3%
 
02/26/2014170.3%
 
04/07/2015170.3%
 
12/27/2013160.3%
 
06/24/2014160.3%
 
Other values (219)86213.5%
 
(Missing)493977.6%
 
2020-12-12T15:30:38.622644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique70 ?
Unique (%)4.9%
2020-12-12T15:30:38.697209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length3
Mean length4.569970159
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n987833.9%
 
a493917.0%
 
0323711.1%
 
/28569.8%
 
126929.3%
 
223678.1%
 
412504.3%
 
35822.0%
 
54441.5%
 
62951.0%
 
71930.7%
 
91910.7%
 
81730.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1481750.9%
 
Decimal Number1142439.3%
 
Other Punctuation28569.8%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0323728.3%
 
1269223.6%
 
2236720.7%
 
4125010.9%
 
35825.1%
 
54443.9%
 
62952.6%
 
71931.7%
 
91911.7%
 
81731.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/2856100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n987866.7%
 
a493933.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1481750.9%
 
Common1428049.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0323722.7%
 
/285620.0%
 
1269218.9%
 
2236716.6%
 
412508.8%
 
35824.1%
 
54443.1%
 
62952.1%
 
71931.4%
 
91911.3%
 
81731.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n987866.7%
 
a493933.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII29097100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n987833.9%
 
a493917.0%
 
0323711.1%
 
/28569.8%
 
126929.3%
 
223678.1%
 
412504.3%
 
35822.0%
 
54441.5%
 
62951.0%
 
71930.7%
 
91910.7%
 
81730.6%
 

Impervious surface present?
Categorical

HIGH CARDINALITY
MISSING

Distinct202
Distinct (%)17.7%
Missing5227
Missing (%)82.1%
Memory size49.9 KiB
no
294 
yes, structure with sidewalk.
152 
yes, structure with sidewalk
109 
No
62 
yes, prior to demolition.
49 
Other values (197)
474 
ValueCountFrequency (%) 
no2944.6%
 
yes, structure with sidewalk.1522.4%
 
yes, structure with sidewalk1091.7%
 
No621.0%
 
yes, prior to demolition.490.8%
 
yes, prior to demolition390.6%
 
yes380.6%
 
No. Advised Water to cancel billing 3-20-14. Dpark310.5%
 
Yes. A house is present.210.3%
 
structure with sidewalk.170.3%
 
yes, house with sidewalk.150.2%
 
NO150.2%
 
yes, structure130.2%
 
structure with sidewalk130.2%
 
yes, structure with sidewalk and driveway.130.2%
 
No.90.1%
 
yes, house with sidewalk80.1%
 
yes house with sidewalk60.1%
 
yes structure with sidewalk.50.1%
 
yes, structure with sidewalk and drive.50.1%
 
yes - structure40.1%
 
yes structure40.1%
 
yes house with sidewalk.40.1%
 
yes, structure with driveway.40.1%
 
yes until demolished.3< 0.1%
 
Other values (177)2073.3%
 
(Missing)522782.1%
 
2020-12-12T15:30:38.779280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique151 ?
Unique (%)13.2%
2020-12-12T15:30:38.870358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length148
Median length3
Mean length6.361551751
Min length2

Overview of Unicode Properties

Unique unicode characters59
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n1132728.0%
 
a611515.1%
 
31147.7%
 
e28377.0%
 
s20505.1%
 
t18714.6%
 
i15733.9%
 
r14383.6%
 
o12903.2%
 
d10032.5%
 
u9882.4%
 
w9252.3%
 
l7912.0%
 
.6471.6%
 
y6361.6%
 
h6251.5%
 
c5661.4%
 
,4921.2%
 
k4371.1%
 
m2870.7%
 
p2620.6%
 
v1950.5%
 
N1280.3%
 
Y1010.2%
 
A1010.2%
 
Other values (34)7051.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3543487.5%
 
Space Separator31147.7%
 
Other Punctuation11512.8%
 
Uppercase Letter5311.3%
 
Decimal Number1970.5%
 
Dash Punctuation770.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1132732.0%
 
a611517.3%
 
e28378.0%
 
s20505.8%
 
t18715.3%
 
i15734.4%
 
r14384.1%
 
o12903.6%
 
d10032.8%
 
u9882.8%
 
w9252.6%
 
l7912.2%
 
y6361.8%
 
h6251.8%
 
c5661.6%
 
k4371.2%
 
m2870.8%
 
p2620.7%
 
v1950.6%
 
b790.2%
 
f760.2%
 
g570.2%
 
x3< 0.1%
 
j2< 0.1%
 
z1< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.64756.2%
 
,49242.7%
 
/80.7%
 
'20.2%
 
&10.1%
 
:10.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3114100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N12824.1%
 
Y10119.0%
 
A10119.0%
 
D417.7%
 
W336.2%
 
H275.1%
 
O234.3%
 
T234.3%
 
C224.1%
 
S61.1%
 
P61.1%
 
U50.9%
 
I40.8%
 
B40.8%
 
R20.4%
 
E20.4%
 
M20.4%
 
L10.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-77100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14824.4%
 
23919.8%
 
03618.3%
 
33517.8%
 
43517.8%
 
621.0%
 
810.5%
 
910.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3596588.8%
 
Common453911.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n1132731.5%
 
a611517.0%
 
e28377.9%
 
s20505.7%
 
t18715.2%
 
i15734.4%
 
r14384.0%
 
o12903.6%
 
d10032.8%
 
u9882.7%
 
w9252.6%
 
l7912.2%
 
y6361.8%
 
h6251.7%
 
c5661.6%
 
k4371.2%
 
m2870.8%
 
p2620.7%
 
v1950.5%
 
N1280.4%
 
Y1010.3%
 
A1010.3%
 
b790.2%
 
f760.2%
 
g570.2%
 
Other values (18)2070.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
311468.6%
 
.64714.3%
 
,49210.8%
 
-771.7%
 
1481.1%
 
2390.9%
 
0360.8%
 
3350.8%
 
4350.8%
 
/80.2%
 
'2< 0.1%
 
62< 0.1%
 
81< 0.1%
 
&1< 0.1%
 
91< 0.1%
 
:1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII40504100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n1132728.0%
 
a611515.1%
 
31147.7%
 
e28377.0%
 
s20505.1%
 
t18714.6%
 
i15733.9%
 
r14383.6%
 
o12903.2%
 
d10032.5%
 
u9882.4%
 
w9252.3%
 
l7912.0%
 
.6471.6%
 
y6361.6%
 
h6251.5%
 
c5661.4%
 
,4921.2%
 
k4371.1%
 
m2870.7%
 
p2620.6%
 
v1950.5%
 
N1280.3%
 
Y1010.2%
 
A1010.2%
 
Other values (34)7051.7%
 
Distinct2
Distinct (%)0.2%
Missing5201
Missing (%)81.7%
Memory size49.9 KiB
No
652 
Yes
 
514
(Missing)
5201 
ValueCountFrequency (%) 
No65210.2%
 
Yes5148.1%
 
(Missing)520181.7%
 
2020-12-12T15:30:38.927407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Location 1
Categorical

HIGH CARDINALITY
UNIFORM

Distinct6317
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
No Address Assigned By City Kansas City, MO 99999
 
42
4433 Forest Kansas City, MO 64110 (39.04594, -94.571053)
 
2
3211 Brighton Ave Kansas City, MO 64128 (39.066601, -94.525543)
 
2
6542 Charlotte St Kansas City, MO 64131 (39.008308, -94.578537)
 
2
8101 E 55th St Kansas City, MO 64129 (39.024086, -94.490982)
 
2
Other values (6312)
6317 
ValueCountFrequency (%) 
No Address Assigned By City Kansas City, MO 99999420.7%
 
4433 Forest Kansas City, MO 64110 (39.04594, -94.571053)2< 0.1%
 
3211 Brighton Ave Kansas City, MO 64128 (39.066601, -94.525543)2< 0.1%
 
6542 Charlotte St Kansas City, MO 64131 (39.008308, -94.578537)2< 0.1%
 
8101 E 55th St Kansas City, MO 64129 (39.024086, -94.490982)2< 0.1%
 
3814 E 69th Ter Kansas City, MO 64132 (39.000778, -94.542099)2< 0.1%
 
3214 E 7th St Kansas City, MO 64124 (39.104332, -94.543457)2< 0.1%
 
5609 E 33rd Ter Kansas City, MO 64128 (39.06414, -94.518356)2< 0.1%
 
3925 Cleveland Ave Kansas City, MO 64130 (39.054039, -94.539513)2< 0.1%
 
916 Van Brunt Blvd Kansas City, MO 64127 (39.10099, -94.526268)2< 0.1%
 
1835 POPLAR AVE Kansas City, MO 64127 (39.08862005, -94.5245193)1< 0.1%
 
4311 ASKEW AVE Kansas City, MO 64130 (39.04710508, -94.54215302)1< 0.1%
 
3712 E 39TH ST Kansas City, MO 64128 (39.05525298, -94.54035601)1< 0.1%
 
2715 Olive KANSAS CITY, MO 64109 (39.07657, -94.55448)1< 0.1%
 
1918 Topping Ave Kansas City, MO 64127 (39.087212, -94.516327)1< 0.1%
 
5428 MICHIGAN AVE Kansas City, MO 64130.0 (39.02759661, -94.56358929)1< 0.1%
 
8017 Montgall Ave Kansas City, MO 64132 (38.98064, -94.556297)1< 0.1%
 
8036 South Benton St Kansas City, MO 64132 (39.002445, -94.553169)1< 0.1%
 
2015 CYPRESS AVE Kansas City, MO 64128 (39.08663123, -94.53010617)1< 0.1%
 
2524 Hardesty Ave Kansas City, MO 64127 (39.078793, -94.520279)1< 0.1%
 
1109 E BANNISTER RD Kansas City, MO 64125 (38.95483879, -94.57668389)1< 0.1%
 
3341 CHESTNUT AVE Kansas City, MO 64128 (39.06481927, -94.55046346)1< 0.1%
 
2434 Mersington Ave Kansas City, MO 64127 (39.081058, -94.537498)1< 0.1%
 
2309 Olive St Kansas City, MO 64127 (39.084011, -94.554123)1< 0.1%
 
4513 Cypress Ave Kansas City, MO 64130 (39.043499, -94.531952)1< 0.1%
 
Other values (6292)629298.8%
 
2020-12-12T15:30:38.993464image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6307 ?
Unique (%)99.1%
2020-12-12T15:30:39.076535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length96
Median length63
Mean length63.08999529
Min length33

Overview of Unicode Properties

Unique unicode characters68
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
4032610.0%
 
4238615.9%
 
9220905.5%
 
3198204.9%
 
1185754.6%
 
0171874.3%
 
5165274.1%
 
2158403.9%
 
6155373.9%
 
.135073.4%
 
,126933.2%
 
126893.2%
 
8108252.7%
 
a107512.7%
 
s106452.7%
 
7101812.5%
 
A89172.2%
 
O78091.9%
 
t75231.9%
 
C73271.8%
 
M70001.7%
 
K67651.7%
 
n67071.7%
 
-63751.6%
 
(63621.6%
 
Other values (43)6585516.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number17044342.4%
 
Uppercase Letter7031217.5%
 
Lowercase Letter6262315.6%
 
Space Separator4032610.0%
 
Other Punctuation262026.5%
 
Control126893.2%
 
Dash Punctuation63751.6%
 
Open Punctuation63621.6%
 
Close Punctuation63621.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
42386114.0%
 
92209013.0%
 
31982011.6%
 
11857510.9%
 
01718710.1%
 
5165279.7%
 
2158409.3%
 
6155379.1%
 
8108256.4%
 
7101816.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
40326100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A891712.7%
 
O780911.1%
 
C732710.4%
 
M700010.0%
 
K67659.6%
 
S58548.3%
 
E51467.3%
 
T39425.6%
 
N32894.7%
 
I25203.6%
 
V20953.0%
 
Y20062.9%
 
L13411.9%
 
R11821.7%
 
H10461.5%
 
B9651.4%
 
D7761.1%
 
W6120.9%
 
P5970.8%
 
G5380.8%
 
U2790.4%
 
F1890.3%
 
J850.1%
 
Q28< 0.1%
 
X2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1075117.2%
 
s1064517.0%
 
t752312.0%
 
n670710.7%
 
i55398.8%
 
y51798.3%
 
e46177.4%
 
v23263.7%
 
o15642.5%
 
r15592.5%
 
l14502.3%
 
h12011.9%
 
d9181.5%
 
g5981.0%
 
u4540.7%
 
p3800.6%
 
k3110.5%
 
c3110.5%
 
w2310.4%
 
m1260.2%
 
b980.2%
 
f880.1%
 
x370.1%
 
z10< 0.1%
 

Most frequent Control characters

ValueCountFrequency (%) 
12689100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1350751.5%
 
,1269348.4%
 
/2< 0.1%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(6362100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6375100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)6362100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common26875966.9%
 
Latin13293533.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
4032615.0%
 
4238618.9%
 
9220908.2%
 
3198207.4%
 
1185756.9%
 
0171876.4%
 
5165276.1%
 
2158405.9%
 
6155375.8%
 
.135075.0%
 
,126934.7%
 
126894.7%
 
8108254.0%
 
7101813.8%
 
-63752.4%
 
(63622.4%
 
)63622.4%
 
/2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a107518.1%
 
s106458.0%
 
A89176.7%
 
O78095.9%
 
t75235.7%
 
C73275.5%
 
M70005.3%
 
K67655.1%
 
n67075.0%
 
S58544.4%
 
i55394.2%
 
y51793.9%
 
E51463.9%
 
e46173.5%
 
T39423.0%
 
N32892.5%
 
I25201.9%
 
v23261.7%
 
V20951.6%
 
Y20061.5%
 
o15641.2%
 
r15591.2%
 
l14501.1%
 
L13411.0%
 
h12010.9%
 
Other values (25)98637.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII401694100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
4032610.0%
 
4238615.9%
 
9220905.5%
 
3198204.9%
 
1185754.6%
 
0171874.3%
 
5165274.1%
 
2158403.9%
 
6155373.9%
 
.135073.4%
 
,126933.2%
 
126893.2%
 
8108252.7%
 
a107512.7%
 
s106452.7%
 
7101812.5%
 
A89172.2%
 
O78091.9%
 
t75231.9%
 
C73271.8%
 
M70001.7%
 
K67651.7%
 
n67071.7%
 
-63751.6%
 
(63621.6%
 
Other values (43)6585516.4%
 

Interactions

2020-12-12T15:30:27.368460image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:27.434016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:27.497571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:27.566631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:27.635190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:27.701247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:27.769305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:27.834361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:27.901418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:27.964473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.026026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.092583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.160141image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.225197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.292255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.358812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.423368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.493428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.562488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.636551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.709114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.780175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.853238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.923298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:28.994359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.061917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.128975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.200036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.270597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.340657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.412719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.480778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.548836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.615394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.680449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.750009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.818568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.886127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:29.956187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.022243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.088300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.157360image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.227921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.299983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.373546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.444607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.517670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.587730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.658291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.722346image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.785400image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.852458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.919516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:30.984572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.054132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.119188image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.183743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.249800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.314356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.384916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.454477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.522034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.590593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:31.658151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T15:30:39.152100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T15:30:39.281712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T15:30:39.413325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T15:30:39.571461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T15:30:39.782142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T15:30:31.908867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:32.661515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:33.070367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:30:33.530763image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Parcel NumberProperty ClassProperty StatusInventory TypeZoned AsAddressCityStatePostal CodeLatitudeLongitudeCountyNeighborhoodCity Council DistrictSold DateSchool DistrictPotential UseQuiet TitleEnvironmental Cleanup NeededBrush Removal NeededTrash Removal NeededDemo NeededRehab CandidateMarket Value YearMarket ValueSquare FootageAcquisition DateForeclosure YearProperty ConditionProgramStructure TypeStructure Square FootageNumber of BedroomsNumber of Full BathsNumber of Half BathsOff-street parkingGarage presentDate evaluatedImpervious surface present?Building repairable, water service will be neededLocation 1
0JA30740302500000000Residential VacantAcquiredLand BankR-65834 Olive StKansas CityMO6413039.020031-94.558258JacksonBlue Hills5thNaNKansas City Missouri 110Side YardNNNNNN2019.08850.05586.94753504/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalse01/07/2014noNo5834 Olive St\nKansas City, MO 64130\n(39.020031, -94.558258)
1JA29740131800000000Residential VacantAcquiredLand BankR-1.53232 Park AveKansas CityMO6410939.066830-94.556801JacksonKey Coalition3rdNaNKansas City Missouri 110Side YardNNNNNN2019.09650.04287.40340304/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN3232 Park Ave\nKansas City, MO 64109\n(39.06683, -94.556801)
2JA27310070500000000Residential VacantAcquiredLand BankR-2.58809 E 8th StKansas CityMO6412539.100399-94.478630JacksonNorth Blue Ridge4thNaNKansas City Missouri 110NaNNaNNaNNaNNaNNaNNaN2019.03200.04285.96503504/09/2013NaNAwaiting evaluationNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN8809 E 8th St\nKansas City, MO 64125\n(39.100399, -94.47863)
3JA31330092200000000Residential VacantAcquiredLand BankR-2.54026 Montgall AveKansas CityMO6413039.052231-94.552933JacksonOak Park Southwest3rdNaNKansas City Missouri 110AssemblageNaNNaNNaNNaNNaNNaN2019.02500.04507.04513904/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN4026 Montgall Ave\nKansas City, MO 64130\n(39.052231, -94.552933)
4JA28940213600000000Residential VacantAcquiredLand BankR-2.53408 Askew AveKansas CityMO6412839.063721-94.542007JacksonPalestine West And Oak Park Northeast3rdNaNKansas City Missouri 110Side YardNaNNaNNaNNaNNaNNaN2019.02300.04129.05392404/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN3408 Askew Ave\nKansas City, MO 64128\n(39.063721, -94.542007)
5JA32540034300000000Residential VacantAcquiredLand BankR-7.5No Address Assigned By CityKansas CityMO99999NaNNaNJacksonStayton Meadows3rdNaNRaytown 150NaNNNNNNN2019.0300.0195.54305604/09/2013NaNVacant lot or land - little or no useNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNo Address Assigned By City\nKansas City, MO 99999
6JA31910181600000000Residential VacantAcquiredLand BankB1-15444 Jackson AveKansas CityMO6413039.026039-94.537041JacksonSwope Parkway-Elmwood5thNaNKansas City Missouri 110NaNNNNNNN2019.08600.06614.85916704/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN5444 Jackson Ave\nKansas City, MO 64130\n(39.026039, -94.537041)
7JA28420194200000000Residential VacantAcquiredLand BankR-2.52006 Kansas AveKansas CityMO6412739.087799-94.548790JacksonWashington Wheatley3rdNaNKansas City Missouri 110NaNNNNNNN2019.03000.04030.64250004/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalse03/05/2014NaNNo2006 Kansas Ave\nKansas City, MO 64127\n(39.087799, -94.54879)
8JA28130302600000000Residential VacantAcquiredLand BankR-2.5No Address Assigned By CityKansas CityMO99999NaNNaNJacksonWest Blue Valley3rdNaNKansas City Missouri 110NaNNNNNNN2019.02250.04058.21406304/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaNNo Address Assigned By City\nKansas City, MO 99999
9JA29710073200000000Residential ImprovedDisposedLand BankR-1.52810 PARK AVEKANSAS CITYMO6410939.075013-94.556375JacksonKey Coalition3rd10/07/2015 12:00:00 AMKANSAS CITY MISSOURI 110Affordable HousingNNNNNN2012.014000.06542.45302105/31/20132012.0Structure - fair conditionNaNNaNNaN4.0NaNNaNFalseFalse03/02/2015NaNNaN2810 PARK AVE\nKANSAS CITY, MO 64109\n(39.07501294, -94.55637522)

Last rows

Parcel NumberProperty ClassProperty StatusInventory TypeZoned AsAddressCityStatePostal CodeLatitudeLongitudeCountyNeighborhoodCity Council DistrictSold DateSchool DistrictPotential UseQuiet TitleEnvironmental Cleanup NeededBrush Removal NeededTrash Removal NeededDemo NeededRehab CandidateMarket Value YearMarket ValueSquare FootageAcquisition DateForeclosure YearProperty ConditionProgramStructure TypeStructure Square FootageNumber of BedroomsNumber of Full BathsNumber of Half BathsOff-street parkingGarage presentDate evaluatedImpervious surface present?Building repairable, water service will be neededLocation 1
6357JA28430010500000000Residential VacantAcquiredLand BankR-2.53313 E 23rd StKansas CityMO6412739.084042-94.543778JacksonWashington Wheatley3rdNaNKansas City Missouri 110Infill constructionNaNNaNNaNNaNNaNNaN2019.03650.0007944.76590304/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN3313 E 23rd St\nKansas City, MO 64127\n(39.084042, -94.543778)
635830-710-16-22-00-0-00-000Residential VacantReservedKCMHAB3-25220 PROSPECT AVEKansas CityMO64130.039.031292-94.555257JacksonBlue Hills5thNaNKANSAS CITY MISSOURI 110Public PurposeNNNNNN2019.09000.0005330.26444404/28/2015NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN5220 PROSPECT AVE\nKansas City, MO 64130.0\n(39.03129243, -94.55525652)
6359JA28810340600000000Residential VacantAcquiredLand BankR-2.53015 Denver AveKansas CityMO6412839.070091-94.522797JacksonKnoches Park3rdNaNKansas City Missouri 110AssemblageNaNNaNNaNNaNNaNNaN2019.01600.0002836.89607604/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN3015 Denver Ave\nKansas City, MO 64128\n(39.070091, -94.522797)
6360JA29840120100000000Commercial VacantDisposedLand BankB4-53200A Gillham RdKansas CityMO6410939.068481-94.578163JacksonNorth Hyde Park4th01/13/2017 12:00:00 AMKansas City Missouri 110AssemblageNNNNNN2012.0858.000372.47840304/09/2013NaNVacant lot or land - little or no useNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN3200A Gillham Rd\nKansas City, MO 64109\n(39.068481, -94.578163)
6361JA28920020700000000Residential ImprovedDisposedLand BankR-2.53021 E 27th StKansas CityMO6412839.076580-94.546860JacksonSanta Fe3rd10/01/2014 12:00:00 AMKansas City Missouri 110Affordable HousingNNNNNN2012.015053.0006295.81937504/09/2013NaNStructure - good conditionNaNSingle Family1100.02.02.0NaNFalseFalse01/01/2014Yes. A house is present.Yes3021 E 27th St\nKansas City, MO 64128\n(39.07658, -94.54686)
636230-110-04-17-00-0-00-000Residential ImprovedDisposedKCMHAR-1.53539 BROOKLYN AVEKansas CityMO64109.039.061183-94.557569JacksonIvanhoe Northeast3rd03/18/2014 12:00:00 AMKANSAS CITY MISSOURI 110NaNNNNNNNNaN21062.5264699.781215NaNNaNStructure - severely distressedNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN3539 BROOKLYN AVE\nKansas City, MO 64109.0\n(39.06118325, -94.55756854)
6363JA31230190100000000Residential VacantAcquiredKCMHAR-2.54219 E. 41st StreetKansas CityMO64130.039.051060-94.534420JacksonVineyard Northwest3rdNaNKansas City Missouri 110NaNNaNNaNNaNNaNNaNNaN2019.05500.00011769.00000006/06/2014NaNNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN4219 E. 41st Street\nKansas City, MO 64130.0\n(39.05106011, -94.53441992)
6364JA29740282000000000Residential VacantAcquiredLand BankR-1.53427 Garfield AveKansas CityMO6410939.063221-94.558746JacksonKey Coalition3rdNaNKansas City Missouri 110Side YardNNNNNN2019.03200.0001424.26781204/09/2013NaNVacant lot or land - little or no useNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN3427 Garfield Ave\nKansas City, MO 64109\n(39.063221, -94.558746)
6365JA29630283900000000Residential VacantDisposedLand BankR-1.52622 Woodland AveKansas CityMO6410839.078369-94.561951JacksonWendell Phillips3rd10/17/2018 12:00:00 AMKansas City Missouri 110Infill constructionNNNNNN2012.0168.0003036.06215304/09/2013NaNVacant lot or land - usableNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN2622 Woodland Ave\nKansas City, MO 64108\n(39.078369, -94.561951)
636628-610-16-10-00-0-00-000Residential VacantACQUIREDLAND BANKR-2.56800 E 20TH STKANSAS CITYMO6412839.084660-94.503184JACKSONCentral Blue Valley And Park Tower Gardens3RDNaNKANSAS CITY MISSOURI 110NaNNNNNNN2019.06050.00033199.92000003/01/20182017.0NaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN6800 E 20TH ST\nKANSAS CITY, MO 64128\n(39.08466, -94.503184)